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\chapter{4}{Demand Analysis}
\label{volume:operations:chapter:demand-analysis}
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\quotation{The essence of mathematics is not to make simple things complicated, but to make complicated things simple.}{Stan Gudder, mathematician}
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The analysis of the potential customer demand for the planned BRT system is the foundation for most of the subsequent planning, design, and financial work. Demand estimates are critical to designing the system, planning operations, and forecasting the financial viability of the new system. Knowing where and when customers require transport services will help shape a system that is based, above all, on the \emphasis{needs of customers}.\par
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It is often tempting for a decision maker to want to put a new BRT system on a wide road or a ring road where there is plenty of space, but where demand is limited. Other times, decision makers will choose BRT corridors for political reasons, like putting one BRT corridor in each district, regardless of the relative importance of the corridor to riders, or locating the BRT system where its benefits would accrue to politically powerful people. In the United States, sometimes planners put BRT in places where they hope it will stimulate economic development, but where this development may be a decade away and current ridership is too low to justify the investment. While such factors will inevitably be a part of the decision-making process, BRT planners generally suggest putting a BRT system in a location that will benefit the most customers in the best way possible as quickly as possible, most directly through \emphasis{time savings}.\par
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The first use of demand analysis, discussed in Chapter 5, is generally to find out where the existing\par
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Section 4.1 provides an overview of demand analysis—what is required and what is to be taken into consideration to properly accomplish it. It also stresses the need to gain a thorough understanding of current operations before attempting to improve on them.\par
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Section 4.2 discusses the data collection effort required to support demand estimation, as it is necessary to gain a solid base for forecasting.\par
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Section 4.3 describes a methodology to provide a \emphasis{rapid demand assessment}. Rapid demand assessment will provide an approximate idea of likely BRT demand on major corridors using only traffic counts and occupancy surveys in key locations, accompanied by boarding and alighting and bus-speed surveys. With this information alone, a skilled BRT planner may be able to develop a reasonable demand estimate, but the BRT planner’s previous experience is paramount.\par
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Section 4.4 discusses how to estimate demand more accurately as a transfer from other public transport modes; this is achieved using a \emphasis{public transport model}.\emphasis{ }The public transport model simulates only the public transport system, and can be strengthened with the addition of a customer origin and destination survey. With a basic public transport model, most critical decisions about the BRT system and many critical operational decisions can be made, but the public transport model can only roughly estimate impacts of the system on mixed traffic and on modal shift. Most BRT planners, including the team that designed TransMilenio, primarily use a public transport model.\par
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Section 4.5 discusses the basics of developing a multimodal\emphasis{ transport demand model} for BRT. Such a model will provide full flexibility for testing multiple routing and pricing scenarios, a more robust estimate of plausible modal shift, emissions impacts, bus route optimization, and a host of other useful tools.\par
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Finally, all forecasts are subject to uncertainty and risk, and decision makers must understand these when planning a BRT system. The two main risks in demand estimation are the overestimation of demand, thus requiring more infrastructure and fleet than necessary and exaggerating revenues; and the underestimation of demand, thus limiting the performance and success of the BRT system. Section 4.6 discusses these issues and suggests a few ways to handle them.\par
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\section{1}{Overview of Demand Analysis}
\label{volume:operations:chapter:demand-analysis:section:overview-of-demand-analysis}
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\quotation{It is far better to foresee even without certainty than not to foresee at all.}{Henri Poincaré, French scientist and philosopher, 1854–1912}
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The objective of demand analysis is \emphasis{demand forecast}, though in terms of choosing an appropriate corridor in a city for BRT it is about comparing alternative selections to determine where there will be the greatest return on investment. Demand forecasting for a new mode of transport requires a combination of sound analytic skills, the use of the right approach for each situation, and a good deal of experience with and understanding of public transport demand and operations. Experience and understanding are more important than the use of advanced modeling tools. Even more important than experience and understanding is having data; no amount of expertise can make up for not having enough information—or worse, having unreliable data from a poorly conceived survey.\par
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For all purposes (as operation and infrastructure design, business plan) the forecast demand will be sufficiently described by \emphasis{boardings and alightings to each route in each stop\slash{}station} for:\par
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\begin{itemize}
\item Hours of the day;% @ guide/04-demand-analysis/01-overview-of-demand-analysis.src: lines 18-19: code points 1-0
\item Days of the week;% @ guide/04-demand-analysis/01-overview-of-demand-analysis.src: lines 19-20: code points 1-0
\item Seasons of the year;% @ guide/04-demand-analysis/01-overview-of-demand-analysis.src: lines 20-21: code points 1-0
\item Future years.% @ guide/04-demand-analysis/01-overview-of-demand-analysis.src: lines 21-21: code points 1-19
\end{itemize}
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Ideally, demand forecasting for a BRT system should be undertaken by professionals who are familiar with the existing public transport. If not, then the first step will be for planners to familiarize themselves with the current public transport services and how they are used by customers. When working in developing countries, one must try to understand what may appear, at first sight, to be a somewhat chaotic and dangerous arrangement. There is always some logic behind the apparent chaos of paratransit and semiformal public transport; these will often provide a service with greater frequency and fewer transfers than a full-blown BRT system. Of course, these services are often uncomfortable, dangerous, and perhaps expensive, but one must bear in mind that the new BRT system should be an improvement for the user not just in better buses but also in terms of journey speed, waiting time, comfort, and safety. In order to make sure that the new BRT is an improvement, professionals must experience and understand the existing system.\par
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\beginbox{1}{Models}
\label{chapter:demand-analysis:box:models}
A model is a simplified representation of the real world systems that allows projections of future conditions. Transportation modeling is quite commonly utilized to determine expected demand for proposed supply conditions of future infrastructure supporting policy measures. Modeling helps project future transport growth, as well as allowing planners to run projections across many different scenarios.\par
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However, it should be noted that transportation models do not solve transport problems. Rather, the models are tools that provide decision makers with information to better gauge the impact of different future scenarios. The type of scenarios considered and the type of city conditions desired are still very much the domain of public policy decision-making.\par
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In mathematical models, the set of relations is what we call “the model itself” and the fixed values that make the model fit a particular instance of reality are what we call the parameters. In transportation models, the proposed relations are to mimic the travel decision-making process.\par
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A whole commonly gets named after one main part, so both designers and decision makers must carefully understand what a model is (and its parts) as well as what a model is not, and carefully communicate this distinction.\par
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Usually a demand study is accompanied by a main forecast model (made up of several models) and the results of the study are commonly mistaken for the model itself. If provided different input (that may be disclosed after the study or different proposals) the model will give different results from the demand study. At a lower level, the parameters of the model are commonly mistaken for the model itself. A model with the same input but different parameters will result in different results.\par
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\endbox
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When developing demand estimates, there is always a trade-off between cost, accuracy, and timing. A detailed full demand modeling exercise, if done properly, will produce more accurate results, but developing a fully calibrated transport model is time consuming and expensive. Planners often do not have the time or resources to build and calibrate an entire model all at once. Rapid assessment techniques can produce acceptable accuracy faster and at a much lower cost. In choosing a demand estimation technique, the following must be taken into consideration:\par
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\begin{itemize}
\item Amount and type of already-available data;% @ guide/04-demand-analysis/01-overview-of-demand-analysis.src: lines 69-70: code points 1-0
\item The time and resources one has to do the estimate;% @ guide/04-demand-analysis/01-overview-of-demand-analysis.src: lines 70-71: code points 1-0
\item The degree to which demand is likely to be unpredictable due to significant changes in services, rapid changes in land use, or modal split.% @ guide/04-demand-analysis/01-overview-of-demand-analysis.src: lines 71-71: code points 1-145
\end{itemize}
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Many cities have already built travel-demand models, or at least models of some parts of their public transport systems. These models can vary significantly in quality, particularly in the developing world. Often what exists is something built by a team of consultants to justify a particular project or set of projects. These models frequently have quite limited validity (and therefore limited utility). As such, the quality of any existing model should be checked carefully to see whether it yields results that are readily observable on the street. If the model is reasonably good, then a lot of time and trouble can be saved by simply expanding and improving upon that model. If the model is of poor quality, it is usually better to start from scratch.\par
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BRT projects need models of varying degrees of accuracy at different stages in the design process. Corridor selection requires a fairly rough demand analysis, whereas making subtle changes in service requires a higher degree of accuracy and detail. Modeling longer-term impacts on land use and modal shift, or larger areas of a city, is far more difficult. The more difficulty the greater the likelihood of inaccuracy, and the more work required to construct the model.\par
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The authority responsible for developing the BRT system should develop the capacity to do full multimodal transport demand modeling or at least full public transport system demand modeling over time. However, if this capacity does not already exist, it is unlikely that it can be developed at the same time that the agency is engaged in a politically time-bound BRT planning process.\par
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In most cities, time and money are restricted in the early planning phases, and local modeling capacity may be limited. In such circumstances, it is better to develop the modeling capacity of the agency over time, so that the local partners learn how to collect the required data and develop better models. Even with a limited start to the modeling process, the design team will at least have some preliminary information about demand in a timely manner to influence critical early decisions.\par
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\section{2}{Data Collection}
\label{volume:operations:chapter:demand-analysis:section:data-collection}
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\quotation{The price of light is less than the cost of darkness.}{Arthur Nielsen, market researcher, founder of ACNielsen, 1897–1980}
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People travel from an origin address to a destination address, and may take one or more public transport services to get there; some walking and waiting will be required to complete the journey. The existing public transport services will be a good indication of where and how people travel. Therefore, analyzing existing public transport services and the conditions in which they operate is the first step in demand assessment.\par
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A new BRT system is likely to change the combination of services that travellers will use. To understand how this will affect travellers, and whether or not this would be advantageous to them, it will be necessary to learn more about the pattern of trips, the origins, the destinations, and the volumes involved. This will require additional surveys capable of quantifying these patterns and enabling the construction of an origin-destination trip matrix for the study area. This information, combined with data about bus speeds in the network, will help in the design of a better BRT system.\par
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Moreover, many travellers in a city have a level of choice about which transport mode to use, be it taxi (shared or otherwise), motorcycle, or car. Many of the environmental and economic benefits of a BRT system materialize if some of these users are attracted to BRT instead. The rate of attraction of BRT will depend on how good the routes are, and this will vary for different origin-destination pairs. If attracting car and motorcycle users is a key objective of BRT, then a more comprehensive transport model will be needed.\par
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The next subsections discuss the minimum set of data that need to be collected to generate a reasonable estimate of demand for a proposed BRT system. These consist of:\par
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\begin{itemize}
\item The routes of current public transport services; these can be mapped in GIS, transport modeling software, or Google Earth or Google Maps;% @ guide/04-demand-analysis/02-data-collection.src: lines 30-31: code points 1-0
\item The number of customers using key corridors by means of bus-route-frequency counts and visual-occupancy surveys;% @ guide/04-demand-analysis/02-data-collection.src: lines 31-32: code points 1-0
\item Bus frequency, preferably for every public transport route in the city, by direction, and in morning and evening peak periods;% @ guide/04-demand-analysis/02-data-collection.src: lines 32-33: code points 1-0
\item Bus speeds for each road section covered by a potential BRT route;% @ guide/04-demand-analysis/02-data-collection.src: lines 33-34: code points 1-0
\item Speeds of other vehicles on the existing road network;% @ guide/04-demand-analysis/02-data-collection.src: lines 34-35: code points 1-0
\item Boarding and alighting surveys (and supplementary spot counts at bus stops), to get a first impression of demand patterns.% @ guide/04-demand-analysis/02-data-collection.src: lines 35-35: code points 1-128
\end{itemize}
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In order to develop an improved public transport model it will be necessary to collect additional information; this is discussed in Section 4.5. For a formal, comprehensive transport model even more data will be needed, as discussed in Section 4.6.\par
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\subsection{1}{Route Maps}
\label{volume:operations:chapter:demand-analysis:section:data-collection:subSection:route-maps}
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The first step is to simply map, or test the accuracy of any existing maps, of the current route structure of bus and minibus services. While in developed countries existing bus route maps are increasingly available in one form or another, it is surprising how difficult it often is, particularly in developing cities, to get even a basic up-to-date bus map. Mapping the existing public transport routes provides the first indication of routes with significant customer demand. While the roads that carry the most bus or minibus routes do not always correspond to the highest number of public transport customers on a given corridor, usually there is a strong correlation. If public transport routes are fairly well regulated, then municipal officials should already possess detailed route-itinerary information through registration records and maps of existing routes, but these records are almost always not fully reliable. In many developing-nation cities, the majority of public transport customers may be served by minibus operations that are weakly regulated. In such cases, there may not even be official records of specific routes. In other cases, registered routes and fleets may bear little resemblance to the actual situation.\par
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A growing number of cities, particularly in the developed world, are putting their bus routes and schedules into GTFS or “General Transit Feed Specification.” Originally called “Google Transit Feed Specification,” this is a standard data format that a growing number of US public transport authorities are using to map and publish their current bus routes and bus schedules. So GTFS data is also usable as a map of the existing scheduled bus routes. Often the data entry is imperfect, so the accuracy of the routes has to be checked carefully. Other cities or public transport authorities may have already coded their existing bus routes into Geographic Information System (GIS) software or a travel demand model software.\par
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		\fignote{1}{The figure above is a map of all the bus routes affecting the Maryland Route 355 Corridor in Montgomery County, Maryland, taken from GTFS data.\label{chapter:demand-analysis:figure:the-figure-above-is-a-map-of-all-the-bus-routes-affecting-the-maryland-route-355-corridor-in-montgomery-county-maryland-taken-from-gtfs-data}}{ITDP.}
	}
\fi

Where neither of these data sources is available, it is best to simply ride all of the public transport routes using a simple Global Positioning System (GPS) device to record the geographic coordinates of all official bus stops and popular informal stops. Virtually all smartphones nowadays come with GPS and can be used as trackers, and a simple tracking device—with no screen, but capable of registering one day of movement each 10 seconds)—costs less than US\$10. Apps for smartphones allow data, like bus stop information, to be entered immediately and tagged to the map. The issue with using these in the field will be the short battery life of phones compared to the batteries of strictly GPS devices. But with a GPS, more data cleanup will be necessary, such as labelling the bus stops.\par
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The work can also be done without GPS devices, with printed estimated-route maps distributed to survey personnel, who will document bus stops along the route and make corrections to paper maps. GPS-based surveys will produce speed information as long as a travel log is associated with the survey, while performing a survey with paper maps will require that travel times be written down.\par
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Depending on the data that is already available, itinerary surveys can be quite a big job in large cities, as they require developing a naming\slash{}coding system for the bus stops, identifying all of the routes, mapping the routes, preparing and processing survey data, and accounting for route variations. In a surprising number of cities this will be the only accurate public transport map in the city. Google Earth’s “.kml” format is an excellent way to store bus route map data, and the satellite imagery provides great assistance in mapping routes.\par
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The map in Figure 4.1 is one of the first efforts to map the existing minibus Car Rapide and Ndiaga Ndiaye in Dakar, Senegal. This activity is often a critical first step toward bringing such services into a transparent regulatory framework.\par
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\ifxetex
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		\img{\hsize}{operations/assets/64838d0d9e15e3a66b7cbe1465808fa88e79cb30.jpeg}
		\fignote{2}{Map of 2004 minibus routes in Dakar.\label{chapter:demand-analysis:figure:map-of-2004-minibus-routes-in-dakar}}{CETUD.}
	}
\fi

Once the coordinates of the bus and minibus routes have been collected and mapped, these itineraries can then be input into GIS and\slash{}or transportation modeling programs, with supplementary input from associated spreadsheet or database files; see, for example, Figures 4.2 and 4.3.\par
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With just this amount of information, the main potential BRT corridors should already be obvious in most cities: the corridors where many bus and minibus routes converge.\par
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		\img{\hsize}{operations/assets/3a5a5e35ed8d4687978695f231d458670f04afd6.png}
		\fignote{3}{Software packages can be used to help model demand and can produce visual representations such as the above example from Yichang, China.\label{chapter:demand-analysis:figure:software-packages-can-be-used-to-help-model-demand-and-can-produce-visual-representations-such-as-the-above-example-from-yichang-china}}{ITDP.}
	}
\fi

\subsection{2}{Vehicle and Customer Counts}
\label{volume:operations:chapter:demand-analysis:section:data-collection:subSection:vehicle-and-customer-counts}
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With the basic public transport route structure in hand, bus and minibus counts are the next step in translating bus-route frequency and occupancy numbers into rough public transport demand estimates. These counts can be used to calibrate an existing traffic model, to understand the public transport demand across the city, or to plan services along an already-identified corridor. The first step is to have a reasonable understanding of demand across the network, and then to focus on a particular corridor. The number of buses (or other types of public transport vehicles) per route, combined with their estimated occupancy rates (discussed in the next section), will already yield a crude estimation of a corridor’s existing demand (Figures 4.4 and 4.5).\par
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		\img{\hsize}{operations/assets/e0a95624596f7347046317ecc2e01db32477359e.png}
		\fignote{4}{Example of a visual occupancy data collection sheet that was utilized in Dar es Salaam, Tanzania. Surveyors were trained to estimate the number of customers on a bus, according to its size. This method has no disadvantage when compared with the traditional method of informing occupancy percentage in increments of 25 percent.\label{chapter:demand-analysis:figure:example-of-a-visual-occupancy-data-collection-sheet-that-was-utilized-in-dar-es-salaam-tanzania-surveyors-were-trained-to-estimate-the-number-of-customers-on-a-bus-according-to-its-size-this-method-has-no-disadvantage-when-compared-with-the-traditional-method-of-informing-occupancy-percentage-in-increments-of-25-percent}}{ITDP.}
	}
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		\img{\hsize}{operations/assets/fd38fc0d116c9a6dbeccc9bb4786c14168809a50.jpeg}
		\fignote{5}{Example of a public transport count data collection sheet that was utilized in Dar es Salaam.\label{chapter:demand-analysis:figure:example-of-a-public-transport-count-data-collection-sheet-that-was-utilized-in-dar-es-salaam}}{ITDP.}
	}
\fi

The strategic selection of the points to conduct the traffic and occupancy survey will determine the extent to which the survey results will represent the actual situation. Determining where to do traffic counts can be more of an art than a science, but some general rules can be applied. Generally, the survey locations should allow most trips to be easily captured with a minimum of resources and effort.\par
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When designing a BRT system we are looking for the highest potential public transport demand routes; traffic counts should be conducted in locations where public transport demand may be high. Using the map of existing bus and minibus routes, counts should be done on arterials where many routes converge. Arterials where local knowledge or simple observation indicates high volumes of buses should be counted as well. If a city has a fairly clearly defined central business district (CBD), and most of the trips start or end in the CBD, then it is sometimes possible to do traffic counts at the entry points along a “cordon” around the CBD. For example, in Dar es Salaam, the entire CBD can only be entered through five major arterials and by ferryboat, and few trips both originate and end within the CBD. By conducting traffic counts at just these six entry points, it was possible to obtain precise CBD demand data for each major arterial as well as the collective totals, and the frequency of nearly all bus routes in the city.\par
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If travel into an area is fairly concentrated along a single direction, perhaps from north to south or from east to west, then the conditions may allow an even more selective application of counts. Cordon counts and screen line counts follow the same overall principle: while “cordon line” refers to a surrounding area, a “screen line” refers to the divide of an area into two sides (usually along a river or a train line) to learn the flow passing from one side to another.\par
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Once it is determined that public transport ridership is concentrated along a few key corridors where there is potential to build BRT infrastructure, it is a good idea to do frequency and occupancy counts at several points along this prospective corridor, which also helps determine appropriate corridor length.\par
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Ideally the frequency and occupancy counts should be done for each bus and minibus route. Even if the transportation department or the bus authority provides the bus route by bus-route frequency information and ridership information, it is always essential to do the counts anyway, because the data provided is rarely completely reliable.\par
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Even if the project team does not plan to eventually use a traffic model, it is a good idea to do a number of counts at a larger selection of critical points all around the city, strategically chosen based on a rough estimate of those locations where most daily trips would pass. These counts will be important in calibrating the public transport model. The model will try to predict existing public transport ridership in each corridor, but it may make inaccurate predictions and need to be adjusted; the counts are needed to determine if the model is an accurate reflection of reality.\par
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One does not need to count so many sites that it becomes cost-prohibitive. For example, in the city of Dar es Salaam (population of approximately 2.5 million inhabitants at the time, nowadays more than 4.3 million) traffic counts in about thirty locations captured a large majority of the trips, and in Jakarta (metropolitan population of above 25 million inhabitants) bidirectional counts in about 100 locations were sufficient.\par
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Often, when one is doing counts of public transport vehicles, one also does counts for the rest of the traffic. Later, these counts will be useful in calibrating more complex demand models, developing rough estimates of impacts on level of service, and other uses. (See Figures 4.5, 4.6, 4.7, and 4.8.)\par
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		\fignote{6}{Basic counts of private vehicles help estimate the potential for mode shifting, as well as indicating possible congestion impacts of the dedicated busways. Image of Bangkok, Thailand\label{chapter:demand-analysis:figure:basic-counts-of-private-vehicles-help-estimate-the-potential-for-mode-shifting-as-well-as-indicating-possible-congestion-impacts-of-the-dedicated-busways-image-of-bangkok-thailand}}{Lloyd Wright.}
	}
\fi

This information will also provide an important first clue as to how many customers might switch from private cars or motorcycles to public transport as a result of the new BRT system. Such data will be important to estimating projected greenhouse gas emission impacts, which may be critical to eligibility for Global Environmental Facility (GEF) funding.\par
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		\img{\hsize}{operations/assets/1fc721d260d93ad8212f44d681e250c0494285de.jpeg}
		\fignote{7}{Survey form for cars and trucks in Dar es Salaam.\label{chapter:demand-analysis:figure:survey-form-for-cars-and-trucks-in-dar-es-salaam}}{ITDP.}
	}
\fi

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		\img{\hsize}{operations/assets/8bb07cfbe9292f2ae2e7f52c0d5c2ba5e05597d7.jpeg}
		\fignote{8}{Survey form for non-motorized modes and motorcycles in Dar es Salaam. Image\label{chapter:demand-analysis:figure:survey-form-for-non-motorized-modes-and-motorcycles-in-dar-es-salaam-image}}{ITDP.}
	}
\fi

Additionally, if a full transport-demand model is later used, then the existing data will be in a form that is readily adaptable to a more inclusive analytic package.\par
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As the complexity of the counting process increases, though, the resources required to obtain an accurate count also increase (Figure 4.9). To identify all vehicles and produce a valid count across multiple traffic lanes, a counting strategy becomes vital. One option is to employ counting teams that involve many people at a single location in order to properly record all vehicle types in each of the lanes. Alternatively, video technology can be utilized to record traffic movement and to allow a more precise count at a later time. The video record allows quality-control sampling to ensure that the counting team is performing at a reasonable level of accuracy. With all counting strategies, survey personnel should be properly trained so that all participants have a common understanding of the task at hand.\par
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		\img{\hsize}{operations/assets/f696306854caa4af8eff3dc0f3e11381b0db2cd7.jpeg}
		\fignote{9}{Effective traffic counts require the right set of personnel, including supervisors of the surveyors, and resources.\label{chapter:demand-analysis:figure:effective-traffic-counts-require-the-right-set-of-personnel-including-supervisors-of-the-surveyors-and-resources}}{Municipality of Osaka.}
	}
\fi

\subsection{3}{Occupancy Surveys}
\label{volume:operations:chapter:demand-analysis:section:data-collection:subSection:occupancy-surveys}
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The number of vehicles is only one part of the demand equation. Knowing the average number of customers in each of the vehicle types at any given time period provides the other half of the demand input data. Given the diversity of possible vehicle sizes, the occupancy data should be categorized and collected by vehicle type. The survey should thus identify vehicles according to their seating numbers or maximum-capacity numbers. For public transport and minibus vehicles, some of the possible categorizations could include:\par
% @ guide/04-demand-analysis/02-data-collection.src: lines 203-207: code points 1-103

\begin{itemize}
\item Seventy-seat bus;% @ guide/04-demand-analysis/02-data-collection.src: lines 209-210: code points 1-0
\item Thirty-five-seat bus;% @ guide/04-demand-analysis/02-data-collection.src: lines 210-211: code points 1-0
\item Sixteen-seat minibus (Figure 4.10);% @ guide/04-demand-analysis/02-data-collection.src: lines 211-212: code points 1-0
\item Seven-seat van\slash{}large rickshaw;% @ guide/04-demand-analysis/02-data-collection.src: lines 212-213: code points 1-0
\item Three-seat auto rickshaw;% @ guide/04-demand-analysis/02-data-collection.src: lines 213-214: code points 1-0
\item Shared taxi.% @ guide/04-demand-analysis/02-data-collection.src: lines 214-214: code points 1-18
\end{itemize}
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\ifxetex
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		\img{\hsize}{operations/assets/de19c288fb7d80cf8d9cb89faca56c2b714feb11.jpeg}
		\fignote{10}{The high flow rate of minibuses along the Soweto Highway in Johannesburg, South Africa, requires an organized counting strategy to get an accurate estimate of vehicles and occupancy levels. Image\label{chapter:demand-analysis:figure:the-high-flow-rate-of-minibuses-along-the-soweto-highway-in-johannesburg-south-africa-requires-an-organized-counting-strategy-to-get-an-accurate-estimate-of-vehicles-and-occupancy-levels-image}}{Lloyd Wright.}
	}
\fi

Ideally, the data on the number of vehicles and the occupancy levels should be collected simultaneously. Usually surveyors record the vehicle type, the number of customers (traditionally a percentage of possible occupancy set at multiples of ten or twenty-five is used, but there is no disadvantage in estimating the exact figure), the route number (if visually evident), and the time of observation. The occupancy provides the basis for a fine estimate of corridor demand. Recording the time is necessary to identify peak and non-peak periods, which should be identified at a resolution of ten-minute intervals. Such a resolution does not disrupt the survey and is enough to identify peak moments, even for a specific route; lower resolutions (five minutes) are not practical and are not of much use, unless we are interested in fluctuations due to traffic-light cycles. Peak and peak of peak period figures are needed for dimensioning the system, especially station capacity (see Chapter 25 for more on station planning).\par
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It is important to have a notation convention for surveyors to enter when they cannot identify the route number (which should be different from “I saw no visible identification”) and to enter when they cannot estimate the occupancy (which is different from “no-occupancy,” which would be zero customers). It is also preferable for a surveyor to pause (and record the pause) due to a distraction rather than trying to keep a poor record while dealing with it. A sampled occupancy survey is better than a bad occupancy survey. Figure 4.11 shows how much more information occupancy surveys reveal in comparison to frequency-only surveys.\par
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		\img{\hsize}{operations/assets/bcd55d3ed08350e44b7fa3d46918d2a3df31de78.png}
		\fignote{11}{Sample graph of bus and customer counts in a single direction at a high demand location on the then-planned Guangzhou BRT in peak and off-peak periods, with customer flows exceeding 20,000 customers and 350 buses per hour in an east-west direction.\label{chapter:demand-analysis:figure:sample-graph-of-bus-and-customer-counts-in-a-single-direction-at-a-high-demand-location-on-the-then-planned-guangzhou-brt-in-peak-and-off-peak-periods-with-customer-flows-exceeding-20000-customers-and-350-buses-per-hour-in-an-east-west-direction}}{ITDP.}
	}
\fi

\subsection{4}{Boarding and Alighting Surveys}
\label{volume:operations:chapter:demand-analysis:section:data-collection:subSection:boarding-and-alighting-surveys}
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		\fignote{12}{A count of boarding and alighting at each bus stop along the prospective BRT corridor will be invaluable information in terms of prioritizing corridors, sizing vehicles, and sizing stations.\label{chapter:demand-analysis:figure:a-count-of-boarding-and-alighting-at-each-bus-stop-along-the-prospective-brt-corridor-will-be-invaluable-information-in-terms-of-prioritizing-corridors-sizing-vehicles-and-sizing-stations}}{Maxwell Wagner Colombini Martins.}
	}
\fi

Once the corridors with the highest levels of public transport demand have been identified through the route mapping and vehicle and occupancy counts, an additional boarding and alighting survey on selected public transport and minibus lines can be conducted (Figure 4.13) to further understand travelling patterns. This same data set will also show how many customers are on the bus at any given time.\par
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Boarding and alight surveys can be conducted in two main fashions:\par
% @ guide/04-demand-analysis/02-data-collection.src: lines 254-254: code points 1-66

\begin{enumerate}
\item Surveys of all routes in one station (surveyor is at the station): one of the most important pieces of information when sizing a BRT station is how many customers will be boarding and alighting at each station, and the best way to estimate this is to see how many customers are currently boarding and alighting buses and minibuses in nearby locations.% @ guide/04-demand-analysis/02-data-collection.src: lines 256-257: code points 1-0
\item {Surveys of all stations of one route (surveyor is on the bus): in this type of survey, surveyors should ride the entire length of each major public transport route during rush hour and record how many people get on and off the bus at each stop. Once this is done for all the routes on a prospective BRT corridor, the boarding and alighting numbers and onboard bus numbers can be consolidated to determine the existing ridership for each stop and each link (bus stop to bus stop). Many good BRT systems have been designed with no more information than this same data set, which will also show how many customers are on the bus at any given time. Additionally this arrangement can:\par
% @ guide/04-demand-analysis/02-data-collection.src: lines 258-258: code points 2-680

\begin{itemize}
\item Survey the network speeds (bus stop to bus stop) and the average speed (discussed below);% @ guide/04-demand-analysis/02-data-collection.src: lines 260-261: code points 2-1
\item Relate every customer’s boarding and alighting stops, by handing numbered cards to customers at the moment of boarding and retrieving them when the customer exits the bus. At each stop the number of the last card handed to customers boarding the bus is written and the cards returned from alighting customers are placed in an envelope used only for that station (this has also been done with surveyors handing bar-coded cards and carrying bar-code readers attached to a raspberry pie with GPS hanging on their belts).% @ guide/04-demand-analysis/02-data-collection.src: lines 261-262: code points 2-0
\end{itemize}
% @ guide/04-demand-analysis/02-data-collection.src: lines 260-262: code points 2-0

}
\end{enumerate}
% @ guide/04-demand-analysis/02-data-collection.src: lines 256-262: code points 1-1

If a city is already using automated passenger counters (APC) or electronic ticket machine data, this data is sometimes useful for getting accurate boarding and alighting data. In the best cases, it can provide bus-stop-specific boarding and alighting numbers hour by hour, for every bus route in the system. Sometimes it is difficult to correlate to specific bus stops, but usually this can be allocated to the nearest bus stop without major loss of accuracy. In a growing number of cities, data from modern fare-collection systems can also be used to get boarding and alighting numbers per bus stop.\par
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		\img{\hsize}{operations/assets/40f23e7855235ae70b2ef07b6a10b5517ca1f04e.png}
		\fignote{13}{An example of peak hour existing boarding and alighting numbers mapped onto some possible BRT corridors based on APC data from Montgomery County, Maryland.\label{chapter:demand-analysis:figure:an-example-of-peak-hour-existing-boarding-and-alighting-numbers-mapped-onto-some-possible-brt-corridors-based-on-apc-data-from-montgomery-county-maryland}}{ITDP.}
	}
\fi

If neither of these technologies is in place, manual boarding and alighting surveys will need to be conducted. Since this survey is relatively time-consuming and expensive, it can be done for a subset of routes in the city; for example, those routes that have a frequency greater than three buses per hour and overlap for more than 10 percent of their length with the BRT corridor. Properly sampled, and in combination with previous survey data, it is possible to draw an internal picture of current public transport system use (i.e., correlated users’ entrance and exit points) in the influence area, in a way similar to that discussed in the next subsection.\par
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		\img{\hsize}{operations/assets/585d87fd9cbeb8a0952aab9c4011b337eb748182.png}
		\fignote{14}{This is the display of a survey result taken in 2006 before the Guangzhou BRT was built. It shows the boarding and alighting along bus route 507. The stops with yellow dots connected with a red line are stops along the planned Zhongshan Road BRT system, with the line at the top showing bus occupancy.\label{chapter:demand-analysis:figure:this-is-the-display-of-a-survey-result-taken-in-2006-before-the-guangzhou-brt-was-built-it-shows-the-boarding-and-alighting-along-bus-route-507-the-stops-with-yellow-dots-connected-with-a-red-line-are-stops-along-the-planned-zhongshan-road-brt-system-with-the-line-at-the-top-showing-bus-occupancy}}{ITDP.}
	}
\fi

\subsection{5}{Methods of Developing an Internal Public Transport Origin-Destination Matrix}
\label{volume:operations:chapter:demand-analysis:section:data-collection:subSection:methods-of-developing-an-internal-public-transport-origin-destination-matrix}
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Recently, BRT system planners have been developing methods for creating an internal public transport origin and destination matrix (OD matrix) based on sampled boarding and alighting and ridership data. In a growing number of cities, ticketing systems collect information about where a specific ticket ID enters a system, but very few systems also collect data about where a specific ticket ID leaves the system. In the more typical case, ticket validation happens upon entering turnstiles, and it is possible to construct an OD matrix by locating the morning ticket-validation point as the trip origin (where the person first enters the system in the morning), and the afternoon ticket-validation point as the destination (where the person enters the system in the afternoon); intermediate validations may indicate transfers depending on analysis of the interval between validations. The main difficulty to this approach is that bus positioning systems and ticketing systems are independent and both generate huge logs, which require a lot of computational power to process (a few hours to process one day of data on a laptop). But this often generates a usable baseline OD matrix with a much larger number of data inputs than any survey method would ever gather. Currently, methods are being developed for doing this in faster ways.\par
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While this, as well as the more detailed boarding and alighting survey, can generate a fairly accurate OD matrix of the boarding patterns in a bus system, it does not reveal the original starting point or eventual destination of customers whose trips involved more than one trip segment or mode, and may not provide a matrix to be directly assigned to the network. For example, if a customer surveyed travels from A to C via B, it will cover both the trips as A-B and B-C separately but will not predict a transfer at B. To determine this information, the onboard survey can be supplemented with an off-board interview survey at major transfer points that needs to be expanded using frequency and occupancy counts.\par
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\section{3}{Basic Methods for Estimating Public Transport Demand}
\label{volume:operations:chapter:demand-analysis:section:basic-methods-for-estimating-public-transport-demand}
% @ guide/04-demand-analysis/03-basic-methods-for-estimating-public-transport-demand.src: lines 1-185: code points 1-9

\quotation{Computers are useless. They can only give you answers.}{Pablo Picasso, artist, 1881–1973}
% @ guide/04-demand-analysis/03-basic-methods-for-estimating-public-transport-demand.src: lines 3-4: code points 1-34

The proper data processing of an appropriate sample size, with a few inference models, shall be enough to describe the demand of the current public transport system in the influence area of the BRT system. Modified by assumptions, this is a reliable indicator of the potential demand for a BRT system.\par
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\subsection{1}{Estimating Demand by Aggregating Boarding and Alighting Data}
\label{volume:operations:chapter:demand-analysis:section:basic-methods-for-estimating-public-transport-demand:subSection:estimating-demand-by-aggregating-boarding-and-alighting-data}
% @ guide/04-demand-analysis/03-basic-methods-for-estimating-public-transport-demand.src: lines 10-119: code points 1-68

The boarding and alighting surveys in section 4.2.4 will give a picture of how many customers are currently on each existing bus route, and how many customers are boarding and alighting at each existing bus stop.\par
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		\img{\hsize}{operations/assets/4127a34eface4db3627bee3c578854ae9d5d5901.png}
		\fignote{15}{Aggregate data for boarding and alighting at current bus stops, allocated to future Guangzhou BRT stations (passengers per hour) in May 2009. (The system opened in February 2010.)\label{chapter:demand-analysis:figure:aggregate-data-for-boarding-and-alighting-at-current-bus-stops-allocated-to-future-guangzhou-brt-stations-passengers-per-hour-in-may-2009-the-system-opened-in-february-2010}}{ITDP.}
	}
\fi

This is done by simply adding up the boarding and alighting numbers from each of the bus routes at each stop, and calculating the onboard customers along each link. For cities that do not have defined bus stops, the BRT planner can divide the entire network into segments and total customer boarding and alighting at each segment.\par
% @ guide/04-demand-analysis/03-basic-methods-for-estimating-public-transport-demand.src: lines 19-22: code points 1-26

\ifxetex
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	{  % group required to avoid fignote settings escaping
		\img{\hsize}{operations/assets/c7280c420c819446c4077613a238a84624342086.png}
		\fignote{16}{The form above, from an MS-Access database, contains information about current bus stops stored in ITDP’s preliminary demand model, used in the Guangzhou BRT planning. The lower portion of this form shows which BRT station the customer demand using stop “zs021” is allocated to. Twenty-five percent of the demand is allocated to the Shipaiqiao BRT station (“zsb02”); and 75 percent of the demand is allocated to the Gangding BRT station (“zsb03”). The allocation is based primarily on proximity, but also takes into account intersections and major demand points.\label{chapter:demand-analysis:figure:the-form-above-from-an-ms-access-database-contains-information-about-current-bus-stops-stored-in-itdps-preliminary-demand-model-used-in-the-guangzhou-brt-planning-the-lower-portion-of-this-form-shows-which-brt-station-the-customer-demand-using-stop-zs021-is-allocated-to-twenty-five-percent-of-the-demand-is-allocated-to-the-shipaiqiao-brt-station-zsb02-and-75-percent-of-the-demand-is-allocated-to-the-gangding-brt-station-zsb03-the-allocation-is-based-primarily-on-proximity-but-also-takes-into-account-intersections-and-major-demand-points}}{ITDP.}
	}
\fi

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		\img{\hsize}{operations/assets/669a2b5f404919de4945b8b2d28cb8441a2d4cb8.png}
		\fignote{17}{The database form for the Gangding BRT station (in this case the southern platform of Gangding station, serving west-to-east direction) shows that 75 percent of bus stop zs021’s demand has been allocated to Gangding BRT station. (The remaining 25 percent was allocated to the previous station, Shipaiqiao.)\label{chapter:demand-analysis:figure:the-database-form-for-the-gangding-brt-station-in-this-case-the-southern-platform-of-gangding-station-serving-west-to-east-direction-shows-that-75-percent-of-bus-stop-zs021s-demand-has-been-allocated-to-gangding-brt-station-the-remaining-25-percent-was-allocated-to-the-previous-station-shipaiqiao}}{ITDP.}
	}
\fi

This was part of the approach used by ITDP working with the Guangzhou Municipal Engineering Design and Research Institute to plan the operations and infrastructure in the Guangzhou BRT system.\par
% @ guide/04-demand-analysis/03-basic-methods-for-estimating-public-transport-demand.src: lines 36-37: code points 1-93

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		\img{\hsize}{operations/assets/68760eb91a46396ffa4d18f5e25653f8c544470d.png}
		\fignote{18}{Projected morning peak customer flows along the Guangzhou BRT, with BRT customer volumes marked for each link along the corridor.\label{chapter:demand-analysis:figure:projected-morning-peak-customer-flows-along-the-guangzhou-brt-with-brt-customer-volumes-marked-for-each-link-along-the-corridor}}{ITDP.}
	}
\fi

The same data can be used to calculate the total customers onboard BRT vehicles along each link of a BRT corridor, and from this the existing maximum customer load at the critical link can be identified (Figure 4.18).\par
% @ guide/04-demand-analysis/03-basic-methods-for-estimating-public-transport-demand.src: lines 42-44: code points 1-14

In cities that have both APC data and GTFS data, it is also possible to attribute the APC boarding and alighting data to each route mapped on GTFS to create a very detailed link-by-link, hour-by-hour map, and table of existing public transport demand. This process is currently quite labor intensive, as most GTFS data is not that clean and APC data is not that easy to attribute to specific bus stops. However, it is still faster and cheaper than doing system-wide boarding and alighting surveys. As cities standardize data input processes, and protocols are established for this, it should become even faster and cheaper.\par
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		\img{\hsize}{operations/assets/9271c5e248369c55cf579d5092848746bc3c6691.png}
		\fignote{19}{The image above was created for Montgomery County, Maryland, USA, to show the existing peak-hour demand throughout the entire county. This was produced in a matter of months and at a very modest cost.\label{chapter:demand-analysis:figure:the-image-above-was-created-for-montgomery-county-maryland-usa-to-show-the-existing-peak-hour-demand-throughout-the-entire-county-this-was-produced-in-a-matter-of-months-and-at-a-very-modest-cost}}{ITDP.}
	}
\fi

The demand on the future planned BRT system, however, will vary from the existing demand in a few key aspects. First, if not all the existing bus routes will be allowed to continue on the corridor and make use of the BRT infrastructure, then the demand will be only that subset of customers whose former bus routes have been either incorporated into the new BRT system’s service plan, or cut and replaced with a new BRT route. Second, if the speeds on the new BRT system are higher than the existing bus speeds, the new BRT system will gain customers from other bus routes and from other modes.\par
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Ideally, a thorough and well-thought-out BRT service plan would then be developed, and the demand for this specific service plan would be tested. At the early stages of the planning process, though, most BRT system planners use simple rules of thumb to get a preliminary demand estimate.\par
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Normally, each of the existing bus routes that operate along a corridor selected for BRT trunk infrastructure will be ranked based on two parameters:\par
% @ guide/04-demand-analysis/03-basic-methods-for-estimating-public-transport-demand.src: lines 69-71: code points 1-0

\begin{itemize}
\item The percentage of the existing bus route that traverses the corridor;% @ guide/04-demand-analysis/03-basic-methods-for-estimating-public-transport-demand.src: lines 71-72: code points 1-0
\item Frequency (and the occupancy, if possible) of bus routes in each direction.% @ guide/04-demand-analysis/03-basic-methods-for-estimating-public-transport-demand.src: lines 72-72: code points 1-81
\end{itemize}
% @ guide/04-demand-analysis/03-basic-methods-for-estimating-public-transport-demand.src: lines 71-72: code points 1-81

To determine which routes should be included in the system and which routes should be left to operate outside the system, rerouted, or cut, a certain minimum cutoff for the above two parameters is generally used, as was done in Figure 4.19, which applies a policy minimum of four buses per hour and at least 20 percent of the total length inside the BRT corridor, before a route can be considered for inclusion in the BRT system.\par
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Routes with a higher frequency and higher percentage in the BRT corridor are preferred for inclusion in the BRT system. A similar approach was used in selecting the BRT routes in Guangzhou and the Pune, India, region, as well as in Mexico, where the existing bus operator also became the BRT operator.\par
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\ifxetex
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		\img{\hsize}{operations/assets/1e3fe2eaca48492a7f8deb464789b6cd48682816.png}
		\fignote{20}{Peak frequency and percentage of route traversing the corridor. Example of BRT route selection in a proposed BRT corridor in Ulaanbaatar, Mongolia. Image\label{chapter:demand-analysis:figure:peak-frequency-and-percentage-of-route-traversing-the-corridor-example-of-brt-route-selection-in-a-proposed-brt-corridor-in-ulaanbaatar-mongolia-image}}{ITDP.}
	}
\fi

Figure 4.20 is an example from the early planning stages of the Ulaanbaatar BRT system, where, after applying a policy preference for at least four buses per hour and 20 percent of the route length inside the BRT system, the remaining routes could be included in the BRT system. These percentages are not hard and fast rules. The idea is simply to capture as much of the existing public transport demand as possible in the most efficient way for the BRT system. Given that station space is at a premium and that operation of the BRT requires new buses, routes with higher frequencies and percentages in the corridor are prioritized. This is a preliminary step. Detailed subsequent service planning is covered in Chapter 6.\par
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When building a BRT system, some bus stops are going to be eliminated and other stops are likely to be consolidated. Therefore, the current numbers of boarding and alighting customers from the bus routes that are to be included in the system need to be allocated to the new BRT stations. This is generally done by proximity: the existing boarding and alighting trips are allocated to the nearest new BRT station.\par
% @ guide/04-demand-analysis/03-basic-methods-for-estimating-public-transport-demand.src: lines 95-98: code points 1-106

By collating the total number of boarding and alighting customers at each new BRT station stop from each of the existing routes that are likely to be incorporated into the new BRT system, the number of boarding and alighting customers per stop can be estimated. By collating the total numbers of customers on board for each link (taken from the same data set), the maximum load on the critical link of the new BRT system can be estimated. This maximum load will be a critical input into making design decisions for service and infrastructure planning as detailed in the following chapters.\par
% @ guide/04-demand-analysis/03-basic-methods-for-estimating-public-transport-demand.src: lines 101-106: code points 1-78

\ifxetex
	% disabled for now
\else
	{  % group required to avoid fignote settings escaping
		\img{\hsize}{operations/assets/3c7c30cfb27a66f00b041f9733e920055495fd79.png}
		\fignote{21}{Using the same boarding and alighting survey, average vehicle speeds in the corridor can be calculated and arranged in a graph.\label{chapter:demand-analysis:figure:using-the-same-boarding-and-alighting-survey-average-vehicle-speeds-in-the-corridor-can-be-calculated-and-arranged-in-a-graph}}{ITDP.}
	}
\fi

The new BRT system, if designed correctly, should be able to achieve speeds of up to 29 kph on a major arterial if express routes are included, and 17 kph in downtown areas (with local service). Multiplying the number of projected customers by the difference between existing aggregate speeds and the projected BRT operating speed will yield the projected time-savings benefit of the corridor, but planners should keep in mind that it will not gain door-to-door travel time savings. The other main benefit that can be quantified is the fleet saving for BRT operators, taking into account the increase in bus speeds after BRT is implemented, and using bus fleet requirements as a proxy for overall operational costs. These two factors are what convinced the Guangzhou City Government to implement BRT in a heavily congested city center corridor, rather than in a peripheral suburban location.\par
% @ guide/04-demand-analysis/03-basic-methods-for-estimating-public-transport-demand.src: lines 111-119: code points 1-68

\subsection{2}{Mode Shifts}
\label{volume:operations:chapter:demand-analysis:section:basic-methods-for-estimating-public-transport-demand:subSection:mode-shifts}
% @ guide/04-demand-analysis/03-basic-methods-for-estimating-public-transport-demand.src: lines 121-185: code points 1-9

Planners still need to make certain assumptions about how many new customers are likely to be attracted from other modes. To get a robust estimate requires a transport model, but an important clue will be the existing bus and private vehicle speed data. If existing public transport speeds are already at or above 26 kph, it can safely be assumed that the new system will not provide a significant time savings benefit. This lack of time savings will limit the number of new customers attracted to the system, although customers may be attracted for other reasons (safety, security, comfort, fare price, etc.). The lower the existing public transport speeds, the higher the projected modal shift (Figure 4.21). This also extends to private vehicle speeds—if speeds are low and travel times long, people are more likely to find BRT convenient and efficient.\par
% @ guide/04-demand-analysis/03-basic-methods-for-estimating-public-transport-demand.src: lines 123-131: code points 1-25

Another factor to consider when estimating a likely modal shift is the existing modal split. In cities where public transport trips make up a small fraction of total demand, but shared taxis, minibuses, or three-wheelers make up a large share of trips, the potential modal shift is likely to be greater than in systems where the vast majority of trips are already made by bus.\par
% @ guide/04-demand-analysis/03-basic-methods-for-estimating-public-transport-demand.src: lines 133-136: code points 1-68

A final factor is the system design’s effect on mixed-traffic levels of service. If the project sponsor adopts a design that significantly degrades the mixed-traffic level of service yet significantly improves the speed of bus customers, the modal shift will also be higher. Normally, engineers try to avoid this impact, but there are occasional bottleneck situations where tough choices are hard to avoid.\par
% @ guide/04-demand-analysis/03-basic-methods-for-estimating-public-transport-demand.src: lines 138-141: code points 1-93

\ifxetex
	% disabled for now
\else
	{  % group required to avoid fignote settings escaping
		\img{\hsize}{operations/assets/d31ad944d4ca6358be68461888d713ec697831eb.jpeg}
		\fignote{22}{If existing public transport services are slow or inefficient, the upgrading to BRT will produce a significant shift in customers to the new service.\label{chapter:demand-analysis:figure:if-existing-public-transport-services-are-slow-or-inefficient-the-upgrading-to-brt-will-produce-a-significant-shift-in-customers-to-the-new-service}}{Lloyd Wright.}
	}
\fi

Projecting the modal shift using traffic modeling is sufficiently complex that simple rules of thumb may not be nearly as reliable. In BRT systems with speeds at or above 20 kph and considerably above previous bus speeds, the demand will be from 5 to 20 percent higher than the baseline demand for the existing bus system. It is unlikely that short-term modal shift will be more than 25 percent of the baseline demand from existing public transport trips, though the system should be designed to accommodate an increase of 50 percent above existing public transport demand.\par
% @ guide/04-demand-analysis/03-basic-methods-for-estimating-public-transport-demand.src: lines 146-151: code points 1-53

However, in cases of uncertainty (for example, a large number of difficult-to-count shared taxi trips, or a deteriorated bus system that is underused), designing the system to accommodate as much as a 100 percent increase in ridership, or use of full demand modeling, is recommended.\par
% @ guide/04-demand-analysis/03-basic-methods-for-estimating-public-transport-demand.src: lines 153-155: code points 1-78

If the majority of the vehicles on the corridor are buses, then the traffic benefits of the project will be broadly distributed between both bus customers and mixed traffic, but the modal shift impact will be less. If the majority of the vehicles on the corridor are private vehicles, then a busway will tend to have a stronger adverse impact on mixed-traffic speeds, a stronger positive impact on bus speeds, and therefore a bigger potential modal shift impact.\par
% @ guide/04-demand-analysis/03-basic-methods-for-estimating-public-transport-demand.src: lines 157-161: code points 1-52

Making a final determination about potential benefits of the system and potential mode shift requires additional information about current vehicle speeds and congestion points.\par
% @ guide/04-demand-analysis/03-basic-methods-for-estimating-public-transport-demand.src: lines 163-164: code points 1-74

Table 4.2 provides estimates based on observed impacts in BRT systems around the world. These estimates consider only the vehicle mix on each corridor and the level of congestion on those corridors.\par
% @ guide/04-demand-analysis/03-basic-methods-for-estimating-public-transport-demand.src: lines 166-167: code points 1-94

\tabletitle{1}{Projected modal shift impact based on type of BRT corridor}
\label{chapter:demand-analysis:table:projected-modal-shift-impact-based-on-type-of-brt-corridor}
\halign to 30\tablemodule{%
	\vtop{\sffamily\footnotesize\noindent\hsize=21\tablemodule#}\hbox to 0.5\tablemodule{}&
	\hbox to 0.5\tablemodule{}\vtop{\sffamily\footnotesize\noindent\hsize=8\tablemodule#}\cr
	\color{gray75}\bfseries Type of BRT Corridor& \color{gray75}\bfseries Projected Modal-Shift Impact from Private Vehicles \cr
	\noalign{\vskip 1mm} \hline \noalign{\vskip 2mm} 
	\color{gray50}Little congestion, buses > 30 percent of the vehicle fleet&
	\color{gray50}5 percent\cr
	\noalign{\vskip 2mm 
}	\color{gray50}Some congestion, buses around 25 percent of vehicle fleet&
	\color{gray50}10 percent\cr
	\noalign{\vskip 2mm 
}	\color{gray50}Many links congested, buses around 15 percent of vehicle fleet&
	\color{gray50}15 percent\cr
	\noalign{\vskip 2mm 
}	\color{gray50}Very congested, few buses on the corridor&
	\color{gray50}20 percent\cr
	\noalign{\vskip 2mm 
}}
% @ guide/04-demand-analysis/03-basic-methods-for-estimating-public-transport-demand.src: lines 169-185: code points 1-9

\section{4}{Estimating Demand with a Public Transport Model}
\label{volume:operations:chapter:demand-analysis:section:estimating-demand-with-a-public-transport-model}
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 1-1111: code points 1-0

\quotation{The best way to predict the future is to invent it.}{Immanuel Kant, philosopher, 1724–1804}
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 3-4: code points 1-39

Aggregating boarding, alighting, and customer flows, as in the previous section, provide a useful “first cut” approach to demand estimation. However, this data does not provide information on where people wish to travel. In most cases, a new BRT system will make changes in bus routes, and knowing the origins and destinations of trips (as opposed to boarding and alighting) is important to ensuring that the new services are closely aligned with where customers want to go. In particular, full OD information can inform the design of direct services that reduce the need for customers to transfer from one line to another. As a BRT system expands into a multi-route system, there will be many opportunities to significantly improve services and system efficiency by modifying routes and services, and the potential financial savings should more than pay for the additional cost and trouble associated with building a robust public transport model. This requires two new elements: the construction of a trip matrix and a route-choice model.\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 6-15: code points 1-98

This section will describe how to build a basic transport model that models only the public transport system. With this basic public transport model, it will be possible to develop a much more robust estimate of the demand on the existing system. It will also enable the planning team to much more easily test the demand for different alternative scenarios for fares, as well as to optimize operational characteristics.\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 17-20: code points 1-109

In many cities, some sort of transport model will already exist. But these models have generally been created for a specific purpose, and the purpose has rarely been to design a BRT system. Sometimes, existing models were designed for highway or transport departments and are only usable for motor vehicles, with very limited information on the public transport system. Others may have been created for building a metro, and not usable without additional work. Data is also far too often of poor quality. If a good quality transport model already exists, it should be possible to simply put the public transport system and the proposed BRT scenario into the existing model. However, in most cases the BRT team may have to start nearly from scratch. The public transport system should be modeled first, as this will be the most important information for BRT planning.\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 23-31: code points 1-39

\subsection{1}{Choosing a Modeling Software}
\label{volume:operations:chapter:demand-analysis:section:estimating-demand-with-a-public-transport-model:subSection:choosing-a-modeling-software}
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 33-112: code points 1-68

The first step in setting up a public transport model is to obtain transport-modeling software. The development of transportation modeling software has greatly aided the process of transport supply and demand projections. Software models today can greatly ease the modeling process and increase accuracy and precision. But with the array of software products on the market, the transport planner can be left with an overwhelming set of options. Of course, there is no one software solution that is inherently correct. A range of variables will guide the software selection process. These variables include cost, familiarity of municipal staff and local consultants with a particular product, degree of user friendliness sought, degree of precision sought, and the overall objectives of the modeling task. The table below lists a few commonly used software packages on the market today.\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 35-44: code points 1-0

\tabletitle{2}{Options for Transport-Modeling Software}
\label{chapter:demand-analysis:table:options-for-transport-modeling-software}
\halign to 30\tablemodule{%
	\vtop{\sffamily\footnotesize\noindent\hsize=5\tablemodule#}\hbox to 0.5\tablemodule{}&
	\hbox to 0.5\tablemodule{}\vtop{\sffamily\footnotesize\noindent\hsize=5\tablemodule#}\hbox to 0.5\tablemodule{}&
	\hbox to 0.5\tablemodule{}\vtop{\sffamily\footnotesize\noindent\hsize=18\tablemodule#}\cr
	\color{gray75}\bfseries Software Name& \color{gray75}\bfseries Vendor& \color{gray75}\bfseries Comments \cr
	\noalign{\vskip 1mm} \hline \noalign{\vskip 2mm} 
	\color{gray50}EMME&
	\color{gray50}INRO&
	\color{gray50}Good general purpose\cr
	\noalign{\vskip 2mm 
}	\color{gray50}CUBE&
	\color{gray50}Citilabs&
	\color{gray50}Good general purpose\cr
	\noalign{\vskip 2mm 
}	\color{gray50}TransCAD&
	\color{gray50}Caliper Corporation&
	\color{gray50}Good integration with GIS, easy to use\cr
	\noalign{\vskip 2mm 
}	\color{gray50}Visum&
	\color{gray50}PTV&
	\color{gray50}Good general purpose\cr
	\noalign{\vskip 2mm 
}	\color{gray50}QRS II&
	\color{gray50}AJH Associates&
	\color{gray50}Low cost but weaker on PT assignment\cr
	\noalign{\vskip 2mm 
}	\color{gray50}TMODEL&
	\color{gray50}TModel Corporation&
	\color{gray50}Low cost but weaker on PT assignment\cr
	\noalign{\vskip 2mm 
}	\color{gray50}SATURN&
	\color{gray50}Atkins-ITS&
	\color{gray50}Good for meso-simulation for congested vehicle assignment, but no PT assignment\cr
	\noalign{\vskip 2mm 
}	\color{gray50}AIMSUN 6&
	\color{gray50}Transport Simulation Systems&
	\color{gray50}Integrated package for micro, meso, and macro simulation model\cr
	\noalign{\vskip 2mm 
}	\color{gray50}TRANUS&
	\color{gray50}Free software developed by Modelistica&
	\color{gray50}Integrated land use – transport model\cr
	\noalign{\vskip 2mm 
}	\color{gray50}Paramics&
	\color{gray50}SIAS&
	\color{gray50}Microsimulation package with integration capabilities\cr
	\noalign{\vskip 2mm 
}	\color{gray50}VISSIM&
	\color{gray50}PTV&
	\color{gray50}Microsimulation package, good animations, good integration with VISUM\cr
	\noalign{\vskip 2mm 
}}
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 44-93: code points 1-9

The strongest packages for general-purpose planning and design of BRT systems are EMME, CUBE, and VISUM, with TransCad offering close capabilities. All of these are expensive packages. However, in actuality, the most significant costs will be training staff to become familiar and adept with the software package. Older and more-sophisticated modelers, like the flexible Emme, allow staff to easily write subprograms, called “macros.” More and more consultants are now using Emme in combination with other programs with better GIS capability or with micro-simulation facilities. SATURN is a meso-simulation package that efficiently models groups (platoons) of vehicles and treats delays and congestion accurately, but its public transport facilities are too weak for BRT design. Equally, TMODEL and QRS II are weak at modeling public transport demand and are not recommended for BRT.\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 95-103: code points 1-39

TRANUS can model land use and transportation systems. The transportation model can be used separately and is easy to use and calibrate. The land use model might be difficult to use and calibrate, particularly if many land uses are included in the modeling. Paramics and Vissim simulate trip making at a high level of detail, in particular vehicle-by-vehicle, and in some instances include pedestrians and traffic lights. These are very powerful packages for studying priority at junctions and interactions and delays at stops. They should only be used for these purposes and in combination with the macro demand models listed above, as they are not appropriate for BRT-route analysis. AIMSUN 6 is an integrated package that combines these micro-simulation features with micro- and macro-modeling facilities.\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 105-112: code points 1-68

\subsection{2}{Defining the Study Area and the Zoning System}
\label{volume:operations:chapter:demand-analysis:section:estimating-demand-with-a-public-transport-model:subSection:defining-the-study-area-and-the-zoning-system}
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 114-406: code points 1-102

Normally, the study area for a BRT system will be the areas currently served by bus and minibus services. If the decision maker has already preselected a particular corridor as the first BRT corridor, then the catchment area for this corridor will be the study area, but this may produce a lower demand forecast.\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 116-118: code points 1-102

To analyze travel, the entire study area, as well as some locations outside the study area, need to be divided into a number of zones (Figure 4.22). As all origin-destination data will be collected and coded into this zoning system, establishing these zones is an important first step. Usually the zones are based on census tracts or political subdivisions that have been used as the basis of any existing census information or previous origin-destination studies. Using census and other administrative zones that already exist in the city will increase the chance of compatibility with the overlaying of different data types.\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 120-126: code points 1-11

\ifxetex
	% disabled for now
\else
	{  % group required to avoid fignote settings escaping
		\img{\hsize}{operations/assets/2cecfc35e8534ed04bfe7d6d34f24e409d607846.jpeg}
		\fignote{23}{Representation of study area for analysis.\label{chapter:demand-analysis:figure:representation-of-study-area-for-analysis}}{Ortúzar and Willumsen 2011.}
	}
\fi

The information needed for modeling, however, is not exactly the same as the information needed for the census, so some census zones are usually consolidated into bigger zones or broken up into smaller zones. Transport modelers are generally less concerned about information outside the study area. As a result, they tend to consolidate zones outside the study area into fewer, larger zones. This consolidation is a simple matter of adding up the data associated with each zone.\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 131-135: code points 1-67

Typically, modelers need more-detailed information in the city center and\slash{}or along the proposed BRT corridor and its catchment area. So modelers will typically break up census zones into smaller zones, using more-detailed census data if available, or just dividing the zones using their judgment based on aerial photographs (Figure 4.24). Sometimes, households and employment will be concentrated into some parts of a large zone and not others, and it is important to break up the zone to capture this geographic concentration.\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 137-142: code points 1-14

\ifxetex
	% disabled for now
\else
	{  % group required to avoid fignote settings escaping
		\img{\hsize}{operations/assets/fbe8f2dc54608f02ed9c1fc1e3ec054dd95b816f.png}
		\fignote{24}{Geographic area divided into zones for modeling purposes. Zoning system for Dar Es Salaam BRT feasibility study.\label{chapter:demand-analysis:figure:geographic-area-divided-into-zones-for-modeling-purposes-zoning-system-for-dar-es-salaam-brt-feasibility-study}}{ITDP.}
	}
\fi

.\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 145-145: code points 50-50

Selecting the size of the zones and the number of zones is a trade-off between accuracy, time, and cost. The size and number of zones will also depend in part on how the data was collected and how it will be used. Ideally, one would like to have a single zone associated with each BRT station\slash{}stop. However, this is not always feasible and some compromises are needed. For BRT systems in large cities like Jakarta and Bogotá, roughly five hundred and eight hundred zones, respectively, were used to analyze the main relevant BRT corridors. In a smaller city like Dar es Salaam, only three hundred zones were necessary for the main BRT-corridor analysis, though for detailed traffic-impact analysis, the city center was later broken down into an additional twenty zones.\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 147-154: code points 1-50

Table 4.4 lists the number of zones that have been developed for various cities. Note that cities such as London have multiple levels of zones that permit both coarse- and fine-level analyses.\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 156-158: code points 1-0

\tabletitle{3}{Typical Zone Numbers for Modeling Studies}
\label{chapter:demand-analysis:table:typical-zone-numbers-for-modeling-studies}
\halign to 30\tablemodule{%
	\vtop{\sffamily\footnotesize\noindent\hsize=8\tablemodule#}\hbox to 0.5\tablemodule{}&
	\hbox to 0.5\tablemodule{}\vtop{\sffamily\footnotesize\noindent\hsize=5\tablemodule#}\hbox to 0.5\tablemodule{}&
	\hbox to 0.5\tablemodule{}\vtop{\sffamily\footnotesize\noindent\hsize=5\tablemodule#}\hbox to 0.5\tablemodule{}&
	\hbox to 0.5\tablemodule{}\vtop{\sffamily\footnotesize\noindent\hsize=9\tablemodule#}\cr
	\color{gray75}\bfseries Location& \color{gray75}\bfseries Population& \color{gray75}\bfseries Number of Zones& \color{gray75}\bfseries Comments \cr
	\noalign{\vskip 1mm} \hline \noalign{\vskip 2mm} 
	\color{gray50}Bogotá (2000)&
	\color{gray50}6.1 million&
	\color{gray50}800&
	\color{gray50}BRT project\cr
	\noalign{\vskip 2mm 
}	\color{gray50}Jakarta (2002)&
	\color{gray50}9 million&
	\color{gray50}500&
	\color{gray50}Strategic planning zones\cr
	\noalign{\vskip 2mm 
}	\color{gray50}Dar es Salaam (2005)&
	\color{gray50}2.5 million&
	\color{gray50}300&
	\color{gray50}BRT project\cr
	\noalign{\vskip 2mm 
}	\color{gray50}London (2006)&
	\color{gray50}7.2 million&
	\color{gray50}2252&
	\color{gray50}Fine level subzones\cr
	\noalign{\vskip 2mm 
}	\color{gray50}&
	\color{gray50}&
	\color{gray50}\~1000&
	\color{gray50}Strategic planning zones\cr
	\noalign{\vskip 2mm 
}	\color{gray50}Santiago (2009)&
	\color{gray50}5.5 million&
	\color{gray50}\~700&
	\color{gray50}Strategic planning zones\cr
	\noalign{\vskip 2mm 
}	\color{gray50}Montreal Island (2008)&
	\color{gray50}3.4 million&
	\color{gray50}1425&
	\color{gray50}Strategic planning zones\cr
	\noalign{\vskip 2mm 
}	\color{gray50}Dallas-Fort Worth (2004)&
	\color{gray50}6.5 million&
	\color{gray50}\~4900&
	\color{gray50}Strategic planning zones\cr
	\noalign{\vskip 2mm 
}	\color{gray50}Ahmedabad (2009)&
	\color{gray50}5.4 million&
	\color{gray50}1,400&
	\color{gray50}One zone per bus stop\cr
	\noalign{\vskip 2mm 
}	\color{gray50}Pune-Pimpri Chinchwad (2011)&
	\color{gray50}5.3 million&
	\color{gray50}1,855&
	\color{gray50}One zone per bus stop\cr
	\noalign{\vskip 2mm 
}}
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 158-214: code points 1-9

Source: Ortúzar and Willumsen (2011) and ITDP\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 216-216: code points 1-45

Usually, models provide a good level of accuracy for trunk routes, but not necessarily for feeder routes. Feeder routes typically serve an area within a zone, especially outside of the direct catchment area of the BRT, where zones are normally a bit larger. Thus, those movements are not recorded in the model. A very detailed model to account for feeder routes, however, is not practical; so, besides using the modeling tool, the feeder system should be designed based on the existing system (field observation and experience) and allow flexibility to make changes during implementation.\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 218-223: code points 1-72

These zones, and the road network, must be coded into the transport model. This process will not be described here in any detail, as it is a standard function of all transport modeling and is thoroughly described in the documentation of any commercially available transport demand model. The basic points of this process are summarized below.\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 225-228: code points 1-37

Data is usually entered into a transport model either as a point, called a “node,” which has a specific “x” and “y” coordinate, or as a “link,” which is a line connecting two nodes. Normally, each intersection and each major bend in a road are assigned separate nodes. Nodes are usually numbered. Ideally, the x and y coordinates of each node should correspond to the actual latitude and longitude of that node. Making sure these nodes correspond to actual latitude and longitude is called “geocoding.” Geocoding will ensure that data from different sources are consistent.\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 230-235: code points 1-48

Normally roads are broken up into different links. Links are usually named from their origin node and their destination node.\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 237-238: code points 1-23

\ifxetex
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	{  % group required to avoid fignote settings escaping
		\img{\hsize}{operations/assets/ad92f834a659a2da68e8032bbb63c79183262bb8.png}
		\fignote{25}{Nodes and links from the Dar es Salaam public transport model used to design the DART BRT system.\label{chapter:demand-analysis:figure:nodes-and-links-from-the-dar-es-salaam-public-transport-model-used-to-design-the-dart-brt-system}}{City of Dar es Salaam.}
	}
\fi

\ifxetex
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		\img{\hsize}{operations/assets/a15f2aa4ae4ea75317faea9a438572ce8a1b0434.png}
		\fignote{26}{Fixme\label{chapter:demand-analysis:figure:fixme}}{copyright.}
	}
\fi

For example, in Dar es Salaam, there was already an existing GIS map. If no GIS map exists, then staff can utilize a GPS device to record the coordinates of each of these points (Figure 4.25). In Dar es Salaam, the team initially defined 102 nodes, and later increased it to 2,500 important nodes. By the end, the nodes represented most of the important intersections in the city. Each node was recorded in a simple spreadsheet (Table 4.5). Alternately, the street network can be traced over a geo-referenced aerial photo.\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 245-250: code points 1-0

\tabletitle{4}{Node Coordinates in Dar es Salaam}
\label{chapter:demand-analysis:table:node-coordinates-in-dar-es-salaam}
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% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 250-303: code points 1-9

By connecting these nodes, a series of links are defined that represent different roads. For example, in Dar es Salaam, Morogoro Road between Sokoine Drive and Samora Avenue, is a link (the link between the nodes Morogoro Road X Sokoine Drive and Morogoro Road X Samora Avenue). Link data can also be entered into the transport model from an Excel spreadsheet (Table 4.6).\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 305-309: code points 1-0

\tabletitle{5}{Link Data for the Transport Model in Dar es Salaam}
\label{chapter:demand-analysis:table:link-data-for-the-transport-model-in-dar-es-salaam}
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	\color{gray75}\bfseries Link& \color{gray75}\bfseries Node A& \color{gray75}\bfseries Node B& \color{gray75}\bfseries Two directional \cr
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% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 309-390: code points 1-9

These links are generally further defined based on the number of lanes, direction of movement, and other characteristics. However for public transport planning it is not really necessary to further define at this point.\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 392-394: code points 1-14

Zones are generally entered into a transport model based on the nodes of all points that are needed to define the boundary. In an Excel spreadsheet, each zone will just look like a series of nodes defined by their x and y coordinates.\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 396-398: code points 1-29

Once the data is entered into a model, the zone is actually represented by a special type of node called a “zone centroid.” This zone centroid is a node that is used to signify the average characteristics of the particular zone. In Dar es Salaam, for example, in addition to 2,500 nodes along roads, there were another 300 zone centroid nodes. Trips are generated and attracted to these centroids. It is therefore important to know how these centroids are connected in particular to stations in a new BRT design. Normally these zone centroids are in the middle of the zone, but if all the population is concentrated in one smaller part of a zone, it is better to move the zone centroid closer to the population concentration.\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 400-406: code points 1-102

\subsection{3}{Origin-Destination Survey and Matrix}
\label{volume:operations:chapter:demand-analysis:section:estimating-demand-with-a-public-transport-model:subSection:origin-destination-survey-and-matrix}
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 409-1018: code points 1-90

There are two basic ways to create a public transport system origin-destination matrix for public transport customers without doing a household survey. The most common approach is to conduct an onboard origin destination survey of an entire study area. This survey is one of a family of surveys called intercept surveys, where individuals in the process of making a trip are interviewed either on a bus or minibus, or while riding their bicycle, about their trip origin and destination (where they began their trip and where they will end the trip) and also often the purpose of the trip. Planners should remember that this type of survey will not provide estimates for possible mode shift for private vehicles nor induced demand. And if it only applies to a portion of the city, it may not take into account important changes in travel patterns in the public transport system when a BRT system is implemented.\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 411-419: code points 1-73

Another approach that is becoming popular, as mentioned in Section 4.3.5. above, is to do a type of boarding and alighting survey where customers are given a numbered token when boarding a bus or minibus, and then they turn in their token when alighting. In this way, their precise boarding and alighting location is recorded. This creates an OD matrix that is specific to a bus route. When all the bus-route-specific OD matrixes are added up, one has a type of OD matrix that has one main problem: it is unable to distinguish between those customers whose origin and destination are near the bus stop where they boarded or alighted, and those customers who are transferring to some other public transport route or other means of travel. This problem can be partially corrected by conducting extensive transfer surveys at any likely transfer point, and using the transfer ratios to adjust the OD matrix. This method is often faster, cheaper, and easier to conduct, and as a result larger sample sizes are usually viable. There is also a reduced risk of coding errors. It also has its downside. If the transfer points are not very well known, or if buses do not stop in regular locations, many transfers may be missed, leading to distortions. This sort of survey also does not provide useful information about trip purpose, and precise destinations that may be quite far from bus stops.\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 421-434: code points 1-28

Origin-Destination is not always used by transport agencies to forecast ridership. According to a survey conducted for a Transit Cooperative Research Program (TCRP) report on forecasting and service-planning methods, only 29 percent of transport agencies in the United States consider OD data to be a major part of demand estimation. Nearly half (43 percent) consider OD data when forecasting ridership but do not see it as a major part of demand estimation. Twenty-three percent of agencies did not consider OD data at all.\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 436-441: code points 1-7

Overall, the survey found that 63 percent of transport agencies used OD data gathered from onboard surveys and 40 percent of agencies used OD data derived from modeling as a source when forecasting ridership. In comparison, ridership data collected from fare boxes and by ride checks were much more likely to be used. Eighty-six percent of agencies used data from the fare box and 80 percent used occupancy surveys. Ridership data collected by automated customer counters, a relatively new means of data collection, was used by 40 percent of agencies. Other data sources considered were: existing land use patterns (71 percent), forecast land use patterns (54 percent), census demographic data (66 percent), and economic forecasts (31 percent) and trends (29 percent).\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 443-450: code points 1-37

\emphasis{Data Collection for Intercept Survey Approach}\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 452-452: code points 1-47

All the origin and destination information collected will be coded as between the zone centroids of two of these zones, and aggregated based on these zones. A trip between two zones is called an “origin-destination pair,” or \emphasis{OD pair}. The table of all the trips between each OD pair by any given mode, in this case public transport, is called the \emphasis{OD matrix}.\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 454-457: code points 1-44

To conduct an onboard OD survey, public transport users are interviewed either on board a bus or minibus vehicle (in that case it is not an interception point, but a section of a road between two intersections) or at stops and interchanges. Sometimes, with the cooperation of the police, minibus customers can be interviewed very efficiently by having the van driver pull over and allow the customers to be interviewed. In Dar es Salaam, with the cooperation of the police, the planning team, wearing DART (Dar es Salaam Rapid Transit) shirts, stopped Daladalas (paratransit vehicles) and interviewed all of the customers inside (Figure 4.24). Other data besides OD information can also be collected, if appropriate. Other useful information can include the fares paid and the services used, but the questions should be kept as simple as possible. Although it is tempting to ask about waiting times, these are seldom accurately reported by individuals and are best estimated by another method.\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 459-468: code points 1-65

\ifxetex
	% disabled for now
\else
	{  % group required to avoid fignote settings escaping
		\img{\hsize}{operations/assets/a24701e93e9a7df8a45f6419dcbceb7467a15568.jpeg}
		\fignote{27}{Collecting data on origins and destinations and fares paid from customers using the Dar es Salaam “Daladalas” system helped define the operational characteristics of the proposed BRT system.\label{chapter:demand-analysis:figure:collecting-data-on-origins-and-destinations-and-fares-paid-from-customers-using-the-dar-es-salaam-daladalas-system-helped-define-the-operational-characteristics-of-the-proposed-brt-system}}{Maxwell Wagner Colombini Martins.}
	}
\fi

Onboard OD surveys of bus customers typically attempt to focus on customer flows during the morning peak period. However, it can be difficult to avoid capturing nonpeak trips as well, so normally data is collected for approximately four hours around the morning peak, and averages are taken or weighted.\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 474-476: code points 1-102

The survey locations should correspond to the locations where the traffic counts were conducted earlier, if these points were chosen wisely. In the case of Dar es Salaam, the points where the OD surveys were conducted were the same thirty-four points of the original traffic counts. This precision was possible due to the assistance of the police in pulling over vehicles at particular locations. In Jakarta, the surveys were conducted on board the buses and minibuses, so surveys were conducted along key links that corresponded as closely as possible to the points where previous traffic counts had been conducted.\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 478-483: code points 1-99

\emphasis{Sample Size}\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 485-485: code points 1-13

The sample size for intercept surveys depends on the accuracy required and the population of interest. The error for an intercept OD survey is a function of the number of possible zones that a customer might travel when passing through a particular point. As a simple rule, Ortúzar and Willumsen (2011) suggest the following table for a 95 percent confidence, with a margin of error of 10 percent for given customer flows:\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 487-492: code points 1-0

\tabletitle{6}{Sample size for origin-destination surveys}
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% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 492-514: code points 1-9

Usually, on potential BRT corridors, the flows are much greater than 900, so 10 percent of the total customer flow at any given survey point is a reasonable rule. In the case of Dar es Salaam, the average customer flow at the peak hour was 10,000, so 1,000 customers were surveyed at each point, or some 34,000 surveys for all the points. In Jakarta, 120,000 surveys were conducted, but only about 65,000 of the surveys were usable. This quantity was all that was possible with the budget available, and constituted roughly 3 percent of the peak hour flows. In Jakarta, the survey numbers were weighted based on the flows on the corridor.\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 516-522: code points 1-16

Origins and destinations should be recorded as accurately as possible—for example, as the nearest intersection or other key identifier. These locations then have to be attributed to the zone in which they are located, so the origin and destination can be coded to the zone centroids.\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 524-526: code points 1-65

\emphasis{Error Types}\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 528-528: code points 1-13

The data collection process is prone to two types of errors: measurement errors and sampling errors. Measurement errors arise from misunderstandings and misperceptions between the questions asked and the responses of the sampled subjects. Misinterpretation by the interviewer can result in the incorrect listing of a response. Frequently, during an OD survey, for instance, a person will identify the origin and destination of their trip, but neither the interviewee nor the surveyor are able to locate this location within any of the zones on a map. Sometimes surveyors will also not do the work responsibly and will make up answers. There may also be a degree of bias in which respondents answer questions in a manner that represents a desired state rather than a reality.\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 530-537: code points 1-38

Avoiding measurement errors is a complex process that requires a lot of local knowledge, and should start at the survey stage. One method is to ask the interviewee the best local landmark, and have the local staff identify as precisely as possible its location on a map. Another method is to have the interviewees pick their origin and destination from a preselected list of areas and subareas, and specific popular destinations. The latter method will probably avoid a lot of trouble and confusion, but will lose some subtlety regarding walking distances. In countries where street names and neighborhoods are far from standardized, the latter method may be more effective.\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 539-545: code points 1-54

Sampling errors occur due to the cost and feasibility of surveying very large sample sizes. Sampling errors are approximately inversely proportional to the square root of the number of observations, i.e., to halve them it is necessary to quadruple the sample size.\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 547-549: code points 1-59

\emphasis{Origin Destination Matrixes}\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 551-551: code points 1-29

Once each OD pair is coded to specific zone centroids, a separate OD matrix is created for each survey point. For each survey point and each direction, it is simply a matter of adding up the trips surveyed between each OD pair for the peak hour. This raw survey data will give you a preliminary OD matrix for each direction at each survey point. Table 4.8 outlines the general form of a two-dimensional trip matrix.\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 553-556: code points 1-106

\tabletitle{7}{General form for a two-dimensional trip matrix}
\label{chapter:demand-analysis:table:general-form-for-a-two-dimensional-trip-matrix}
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% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 558-639: code points 1-9

Source: Ortúzar and Willumsen 2011.\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 641-641: code points 1-35

From Table 4.8, “T11” indicates how many trips were made within Zone 1; “Tij” indicates the total surveyed trips between Zone i and Zone j; “O1” is the total origins in Zone 1, and “D1” is the total destinations in Zone 1.\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 643-645: code points 1-10

This simple matrix is still not a full OD matrix for the whole city’s public transport trips during the peak hour. To get to that, the number of people surveyed needs to be related to the total number of public transport customers per direction per hour at each survey point. This process is called \emphasis{expanding the matrix}. The total number of public transport customers at the peak hour is taken from the data that was collected earlier at each of the same points using the public transport vehicle-occupancy surveys. For example, in Dar es Salaam, on some corridors 1,000 out of 10,000 hourly public transport customers per direction were collected on some corridors, which yielded an expansion factor of ten. On this matrix, the observed OD trips need to be multiplied by ten to get the total public transport trips at the peak hour. On other corridors, where 1,000 interviews were taken for only 6,000 customer flows, the expansion factor is six, so the surveyed OD trips need to be multiplied by six. Each separate matrix needs to be expanded by its appropriate expansion factor (as indicated in Table 4.9.), or can be expanded using a single expansion factor.\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 647-658: code points 1-24

\tabletitle{8}{OD Matrixes Expanded by Expansion Factor}
\label{chapter:demand-analysis:table:od-matrixes-expanded-by-expansion-factor}
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% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 660-759: code points 1-9

Because the point of each OD survey was chosen to pick up a discrete set of OD pairs, each individual OD matrix will largely cover a different part of the city, but there will be some overlap and therefore the risk of “double counting” trips will happen twice. The individual matrices will have some OD pairs with actual values, and some OD pairs with zero trips (Tables 4.10 and 4.11).\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 762-765: code points 1-101

\tabletitle{9}{OD Matrix \#1 Eastbound Morogoro Road and United Nations Intersection}
\label{chapter:demand-analysis:table:od-matrix-1-eastbound-morogoro-road-and-united-nations-intersection}
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% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 767-832: code points 1-9

\tabletitle{10}{OD Matrix \#2 Southbound Old Bagamoyo Road and United Nations Intersection}
\label{chapter:demand-analysis:table:od-matrix-2-southbound-old-bagamoyo-road-and-united-nations-intersection}
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% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 835-900: code points 1-9

To develop the full OD matrix for public transport trips in Dar es Salaam, a simple estimate would be to take the maximum value for any OD Pair in any observed survey. Strictly speaking, the correct value for each cell where multiple observations are expected depends on , the proportion of trips between origin i and destination\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 902-905: code points 1-56

\emphasis{j} that pass through each intercept point a, on the sampling ratio at each point a (a number between 0 and 1), and on , the number of trips between \emphasis{i} and \emphasis{j} observed at point a. Then, the correct estimator for the number of trips when there are nij points that would have intercepted trips from i to j, is:\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 907-910: code points 1-3

\ifxetex
	% disabled for now
\else
	{  % group required to avoid fignote settings escaping
		\img{\hsize}{operations/assets/ebdc1db74d2cb1eea56e9691a04269573c869085.png}
		\fignote{28}{FIXME\label{chapter:demand-analysis:figure:fixme}}{TODO}
	}
\fi

Of course, if there is only one intercept point for that OD pair (\emphasis{nij} =1) and there is only one useful route from \emphasis{i }to\emphasis{ j }( =1), then the formula reverts to the familiar sample expansion:\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 914-916: code points 1-10

\ifxetex
	% disabled for now
\else
	{  % group required to avoid fignote settings escaping
		\img{\hsize}{operations/assets/3b300629364cdb1dafaa37f8280357bacf4794ae.png}
		\fignote{29}{TODO: math\label{chapter:demand-analysis:figure:todo-math}}{FixMe}
	}
\fi

For illustration purposes in Table 4.12, the values from the previous two tables have been combined to form a complete OD matrix (assuming that only two points are surveyed).\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 920-921: code points 1-100

\tabletitle{11}{OD Matrix Dar es Salaam}
\label{chapter:demand-analysis:table:od-matrix-dar-es-salaam}
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% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 924-989: code points 1-9

This methodology is used to avoid the double (or triple) counting of some trips. This double counting may happen because some journeys may have been intercepted by more than one survey station, either potentially or in the sample. In this case, steps must be taken to avoid exaggerating their importance in the matrix by weighting those cells appropriately—for example, taking the average value of duplicated cell entries. On the other hand, people may go in very different directions to reach the same endpoint, so using this method will undercount the total demand. For more details, consult Ortúzar and Willumsen 2011.\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 991-996: code points 1-94

\emphasis{Validation}\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 998-998: code points 1-12

Due to these distortions, along with measurement and sampling errors, it is usually necessary to undertake corrective actions. A validation process is typically done at the conclusion of the data-collection process in order to provide a degree of quality control.\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 1000-1002: code points 1-48

Validation is usually accomplished by looking at OD pairs route by route, and doing an informal trip assignment, assigning the OD trips to specific public transport routes, and comparing the aggregate total trips to the aggregate trip counts developed from the occupancy surveys and public transport vehicle counts (Figure 4.27).\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 1004-1007: code points 1-21

\ifxetex
	% disabled for now
\else
	{  % group required to avoid fignote settings escaping
		\img{\hsize}{operations/assets/8bb927dbc1c04e0c226b8fdabf75c85f35adc110.png}
		\fignote{30}{Modeled versus observed customer volumes in Jakarta.\label{chapter:demand-analysis:figure:modeled-versus-observed-customer-volumes-in-jakarta}}{Remi Jeanneret.}
	}
\fi

Once the OD matrix has been cleaned and calibrated, the OD matrix can be input into the transport model, and the testing of different scenarios can begin. The OD matrix can also be used to generate an origin-destination map that gives decision makers an overall view of the density of origins and destinations in the city. The OD map will frequently illustrate the extent to which trips are distributed or are centralized within the city. The OD map of Bogotá shows that there is a heavy concentration of trip destinations to the central business district (Figure 4.28).\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 1011-1016: code points 1-40

\ifxetex
	% disabled for now
\else
	{  % group required to avoid fignote settings escaping
		\img{\hsize}{operations/assets/2fadeb56fd4a5fd21c527a6f9a29cced0f2cbf66.jpeg}
		\fignote{31}{Origin-destination map for Bogotá.\label{chapter:demand-analysis:figure:origin-destination-map-for-bogot}}{TransMilenio SA.}
	}
\fi

\subsection{4}{Outputs of the Public Transport Model}
\label{volume:operations:chapter:demand-analysis:section:estimating-demand-with-a-public-transport-model:subSection:outputs-of-the-public-transport-model}
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 1020-1111: code points 1-0

Once the road system and the OD matrix are input into the transport model, different scenarios for the BRT system can be tested. While the output from the public transport model will be used at various points throughout this guide, for the time being it will be used to generate demand estimates for specific BRT system scenarios.\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 1022-1025: code points 1-21

The first step is generally to take a look at the existing public transport demand on all major corridors throughout the city at the peak hour. These results should now yield a much more accurate estimate of total existing public transport demand on all the major corridors in the city. This result is a valuable tool for prioritizing which corridors should be included in the BRT system. Figure 4.29 is a picture of the total existing public transport demand on all of the major corridors in Jakarta.\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 1027-1031: code points 1-87

\ifxetex
	% disabled for now
\else
	{  % group required to avoid fignote settings escaping
		\img{\hsize}{operations/assets/a7d67dc2fd571b7f8b98dbdb5580401c77deaf1e.jpeg}
		\fignote{32}{A graphical illustration of demand volumes from Jakarta.\label{chapter:demand-analysis:figure:a-graphical-illustration-of-demand-volumes-from-jakarta}}{ITDP.}
	}
\fi

These total-demand estimates, or “desire lines,” tell how many public transport customers are \emphasis{currently }on each major corridor. It still does not say anything about how many public transport customers will be on a specific BRT system.\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 1035-1037: code points 1-28

When first coding the existing public transport system into the model, the following additional information will be required:\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 1039-1041: code points 1-0

\begin{itemize}
\item Vehicle capacity (total standing capacity is all that is used);% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 1041-1042: code points 1-0
\item Public transport (this will be a series of links; each direction needs to be coded separately because sometimes bus routes do not go and return on the same roads);% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 1042-1043: code points 1-0
\item Specific location of the bus stops (for most of the network, just assume the bus stops are at the intersections, but the BRT corridor nodes should be added specifically at the bus stop, and the links between the bus stops should be broken into separate links);% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 1043-1044: code points 1-0
\item Speed on each link (this will be taken from the bus speed and boarding and alighting survey);% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 1044-1045: code points 1-0
\item Bus fare (usually the models allow fare distance, and if there is a flat fare leave the distance blank);% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 1045-1046: code points 1-0
\item Bus frequency;% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 1046-1047: code points 1-0
\item Value of time (there are various ways of calculating this value, but in practice this value is based either on interviews with bus customers or typically 50 percent of the hourly wage rate for the typical bus customer).% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 1047-1047: code points 1-225
\end{itemize}
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 1041-1047: code points 1-225

At this point, the scenario to be tested should be carefully defined. In the case of TransJakarta, the scenario was essentially defined through a decision taken by the governor. The governor’s design decision was as follows:\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 1049-1052: code points 1-0

\begin{itemize}
\item TransJakarta would go from Blok M to Kota station with twenty-four stops at specific locations;% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 1052-1053: code points 1-0
\item TransJakarta would have fully segregated lanes and of certain design;% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 1053-1054: code points 1-0
\item TransJakarta would charge a flat fare of Rp. 2,500 (US\$0.30);% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 1054-1055: code points 1-0
\item There would be no feeder buses and no (functional) discount transfers from any existing routes;% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 1055-1056: code points 1-0
\item Ten existing bus lines travelling between Blok M and Kota would be cut; all other bus routes would be allowed to continue to operate in the mixed traffic lanes at curbside bus stops;% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 1056-1057: code points 1-0
\item Fifty-four buses were procured to operate in the system.% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 1057-1057: code points 1-62
\end{itemize}
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 1052-1057: code points 1-62

When coding this BRT scenario into the public transport model, there is a small difference between coding a new BRT link and coding just any other bus route. In order to test some unique elements of the BRT system, the BRT link will be coded as an entirely new road link with special BRT characteristics, rather than assuming that it is a bus line operating on an existing road link\emphasis{ }that is open to paratransit vehicles and other vehicles. This new BRT link in the model will only be coded for use by a specific BRT vehicle that may be a new vehicle category that does not already exist. In the case of Jakarta, these vehicles are only used on the BRT system. This special coding of the BRT link is also required to give this route special fare characteristics, such as the possibility of free transfers between routes when the system expands to more than one route. Thus, coding a new BRT route is no different from coding any public transport route, except:\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 1059-1069: code points 1-0

\begin{itemize}
\item The bus speed will be higher than for routes on the mixed traffic links. The BRT bus speed must be calculated specifically based on the system’s design, but it is generally between 20 and 29 kph (see Chapter 6 for more information);% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 1069-1070: code points 1-0
\item Some new station locations will be created, which will affect walking times;% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 1070-1071: code points 1-0
\item Bus frequencies will be specific to the number of buses and the bus speed;% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 1071-1072: code points 1-0
\item If a lane of mixed traffic is being removed from the existing link, the definition of the characteristics of that link will need to be changed to reflect the loss of a lane. This change will only be necessary for running the full transport model in the future;% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 1072-1073: code points 1-0
\item It may be necessary to adjust the bus speeds downward for all the bus routes that are running in the mixed-traffic lanes. If there is only a public transport model, this will only be an estimated impact. If there is a full transportation-demand model, the model will help calculate this impact.% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 1073-1073: code points 1-300
\end{itemize}
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 1069-1073: code points 1-300

After defining the new BRT links and assigning them a new BRT route with the characteristics reflecting the political decision, the projected demand for this specific scenario can be calculated (Figure 4.30).\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 1075-1076: code points 1-104

\ifxetex
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\else
	{  % group required to avoid fignote settings escaping
		\img{\hsize}{operations/assets/f12c343847728ac0c742ab45fb4c43ea1f9464e7.png}
		\fignote{33}{Demand estimate for TransJakarta Corridor I, Scenario I.\label{chapter:demand-analysis:figure:demand-estimate-for-transjakarta-corridor-i-scenario-i}}{ITDP.}
	}
\fi

In the case of Jakarta, the projected demand on Corridor I for the scenario determined by the governor was tested. Based on the lack of a feeder system and the unwillingness to cut bus routes that ran parallel to the new BRT system, the demand on the new system would not be very high. Because one mixed traffic lane had been removed, while few of the buses in the mixed traffic lanes had been removed, mixed traffic lanes would be more congested. However, due to the lack of a full transport model, the precise scale of this impact was not known. The planning team therefore encouraged the governor to expand the trunk system rapidly, to add feeder buses with free transfers onto the trunk system, and to cut more existing parallel bus routes.\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 1081-1088: code points 1-20

Note that this demand estimate assumed that the new BRT system would only get the trips from existing public transport trips. It did not assume that any trips would be attracted from other modes, as the public transport model alone did not have the capacity to provide much of an answer to this question. Nevertheless, this analysis produced a very good conservative first estimate of the likely demand.\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 1090-1093: code points 1-98

To include some possible modal shift from private vehicles, one can usually use the same rough estimate methodology recommended in Figure 4.2 above. In Bogotá, where a well-designed system actually decongested the mixed traffic lanes, the modal shift impact in the first phase was a modest 10 percent of private-vehicle users to the BRT system. The shift came from both push and pull factors. Most directly, competing routes by existing operators were eliminated or moved to parallel corridors. As the system has expanded, approximately 20 percent of current BRT customers on TransMilenio are former private-vehicle users. In Jakarta, where few bus routes were cut but the level of service for mixed traffic was more adversely affected, the mode shift was higher, at around 20 percent from taxis, cars, and motorbikes. In Ahmedabad, India, where there were very few bus customers originally on the corridor, there was an even higher modal shift (less than 30 percent), mostly from motorized three-wheelers and motorcycles, which before accounted for less than 70 percent of the existing modal split.\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 1095-1105: code points 1-39

With this demand estimate, planners are better able to assess whether the physical designs proposed will have sufficient capacity to handle the projected demand, whether stations will be congested, and whether or not the system is likely to be profitable or operate at a loss. Planners can optimize the proposed-route network to increase load factors and minimize the need for customer transfers.\par
% @ guide/04-demand-analysis/04-estimating-demand-with-a-public-transport-model.src: lines 1107-1111: code points 1-0

\section{5}{Estimating Demand Using a Full-Transport Model}
\label{volume:operations:chapter:demand-analysis:section:estimating-demand-using-a-full-transport-model}
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 1-618: code points 1-0

\quotation{Those who have knowledge, don’t predict. Those who predict, don’t have knowledge.}{Lao Tzu, philosopher, 6th century BC}
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 3-4: code points 1-38

Most BRT systems in the world have been planned using only a public transport model, without having the full transportation system modeled. The lack of full modeling occurs because such modeling is only in its infancy in most developing countries, and it takes time to build up the data, the skills, and resources to develop a full transport-system demand model. Nonetheless, the tools provided by the full transport-demand model are very useful to BRT planning, and if time and resources allow, developing a full transport demand model is worthwhile.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 6-11: code points 1-42

\subsection{1}{Overview}
\label{volume:operations:chapter:demand-analysis:section:estimating-demand-using-a-full-transport-model:subSection:overview}
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 13-71: code points 1-78

By including all possible modes and replicating the decision process of all citizens to travel, in a full-transport model, there will be a much better sense of “potential” customers for the BRT system who may currently be taking motorcycles, private cars, bicycles, or walking. This model better captures the effects of congestion on different points of the network.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 15-18: code points 1-61

Guidelines for how to build and operate a full-transport demand model are beyond the scope of this guide. However, some basic information on transport modeling is included here to give BRT planners a general overview of how these models work, as they are likely to have output of these sorts of strategic models. Full transport models are normally constructed upon variations of the “classic four-stage models” discussed next.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 20-24: code points 1-15

While complex mathematical relationships underpin these full transportation models, the basic premise behind the modeling analysis can be presented in an understandable form to a wide audience. Figure 4.31 outlines the classic transport model. This model still serves as the basis for the various software products that today enable effective transport modeling. There are two distinct moments regarding four-stage models: the model development and the model application. The first is about understanding what factors influence people’s decisions about what trips they make and how they make them; this is done by observing people’s decisions and splitting them into groups that made similar decisions when faced with the same parameters. The second is about proposing new conditions (city growth, transport projects) and simulating the decisions people make under this new condition.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 26-34: code points 1-37

In short, the application of the model consists of the following:\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 36-37: code points 1-0

\begin{itemize}
\item Defining inputs:% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 37-38: code points 1-0
\item Land use (existing or future) that points where activities are developed;% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 38-39: code points 1-0
\item Transport network (existing or future): road network with its features (eventual bicycle network included here), public transport network (modes, itineraries, frequencies). This changes throughout the day (even road directions can change), so typical moments of the day are to be considered.% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 39-40: code points 1-0
\item Calculating the travel matrix (there are usually several “market segment” matrices for each typical moment of the day);% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 40-41: code points 1-0
\item Simulating the matrix (demand) with the network (supply) to evaluate how each part of the system will be effectively used.% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 41-41: code points 1-128
\end{itemize}
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 37-41: code points 1-128

The third step alone is iterative, because as parts of the system get congested, people will try alternative paths.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 43-44: code points 1-6

The second and third steps together are also iterative (less iterations are required) as the travel matrix is a function of the travel times, and congestion will affect the decision to travel.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 46-47: code points 1-92

In the context of planning, the process is iterative, since it is based on the results that one will propose (or foresee) modifications both in land use and in the transport network.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 49-50: code points 1-81

The four-stage model is useful for the development of a strategic plan, because it makes it possible to consider new modes of transport to be created and to evaluate long-term land use changes. It is more useful to use a full transport model when faced with a lack of data from comparable services or services that do not exist yet, as it will allow for multiple scenarios and an iterative process that looks at the potential shifts in demand that result from new or additional services.. According to a survey on fixed-route public transport ridership forecasting and service planning methods appearing in a TCRP, 47 percent of US transport agencies reported using the four-stage model for the purpose of implementing a new system. The remainder of agencies use different means of estimation, or hire consultants to conduct a ridership forecast. Of the agencies surveyed, 11 percent use elasticities to determine the impact of implementing each planned bus rapid transit improvement. Another 11 percent considers the impact of improving existing travel times. Seventeen percent of agencies would hire an analyst to forecast ridership. The remaining 19 percent of agencies indicated that they would not analyze ridership impacts, but many were not currently considering implementing a new mode of public transport service.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 52-64: code points 1-78

The TCRP survey found that 44 percent of agencies used the four-stage model to determine long-term ridership. Thirty-three percent used trend lines, 22 percent considered service level changes, and 14 percent would not conduct such analysis.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 66-68: code points 1-40

\ifxetex
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		\img{\hsize}{operations/assets/a3b19df2ab8218f2698924c217a8c1d2a88ed68c.png}
		\fignote{34}{Inputs and activities example for the development and application of a classic four-stage model for the Rio de Janeiro Master Plan city.\label{chapter:demand-analysis:figure:inputs-and-activities-example-for-the-development-and-application-of-a-classic-four-stage-model-for-the-rio-de-janeiro-master-plan-city}}{Protocubo.}
	}
\fi

\subsection{2}{Sub-Models}
\label{volume:operations:chapter:demand-analysis:section:estimating-demand-using-a-full-transport-model:subSection:sub-models}
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 73-553: code points 1-106

Market segmentation can be a key issue in deriving good modeling results. Different people react in different ways to changes in the transport system. Even the same person may behave in different ways when travelling to work, on business, or during leisure time. These differences affect design when considering the service during peak (mostly journeys to work and school) and off-peak periods (mostly shopping, social, and recreational trips). The proper segmentation of data can be costly since it requires more carefully collected data and greater detail in the modeling process. However, the benefits of segmentation can be a system well-tailored to the needs of the customers.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 75-81: code points 1-56

\emphasis{Trip Generation}\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 83-83: code points 1-17

When applying this model the \emphasis{input} is the land use for a given area (zone), which may be expressed, for example, as:\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 85-87: code points 1-0

\begin{itemize}
\item Number of households per income level and per number of residents;% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 87-88: code points 1-0
\item Number of residents per age;% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 88-89: code points 1-0
\item Level of education;% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 89-90: code points 1-0
\item Employment by activity sector.% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 90-90: code points 1-36
\end{itemize}
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 87-90: code points 1-36

These are usually projected numbers, based on trending projections considering development projects, in a table such as Table 4.13.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 92-93: code points 1-30

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		\img{\hsize}{operations/assets/7de3ae374111242e509369a3ce049ffa417a15c1.png}
		\fignote{35}{Table\label{chapter:demand-analysis:figure:table}}{FixMe}
	}
\fi

The “borders several OD matrices” are the output of this model; by borders we mean the total number of trips with origin and destination in each zone, as in the following:\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 97-98: code points 1-96

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		\fignote{36}{Table\label{chapter:demand-analysis:figure:table}}{FixMe}
	}
\fi

For one future scenario, the number of matrices’ borders generated can easily be over thirty by combining:\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 102-104: code points 1-0

\begin{itemize}
\item \emphasis{Moments of the day:} usually at least morning peak, afternoon peak and off-peak, but sometimes midday and night (this are usually proportionally divided based on traffic and public transport volumes if dynamic allocation will be made);% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 104-105: code points 1-0
\item \emphasis{Trip purpose:} At least work, education, and others, but sometimes this may include: shopping, social, and recreational; health and personal business; and escorting other people;% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 105-106: code points 1-0
\item \emphasis{Income level:} Splitting population into three to five income groups, depending on evidence of behavioral gaps observed in the model development.% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 106-106: code points 1-152
\end{itemize}
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 104-106: code points 1-152

Additional classification by person type can be included, typically focusing on personal characteristics that may include car ownership levels, household size, and household structure along with other factors such as residential density that play a role in determining the number of trips produced per household.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 108-110: code points 1-103

The development of the model consists in analyzing the mobility rate for trip purpose, including employment sector and school level for each market segment, normally on data from household and\slash{}or workplace surveys.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 112-113: code points 1-106

This analysis is complemented by land use census data (population, employment, education, and specific activity sectors from commerce, industry, and health agencies and unions) and crossed with current trip estimates based on traffic and public transport surveys and transportation sector operation reports.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 115-117: code points 1-100

The trip generation model is usually is divided into two other models: \emphasis{production model} and \emphasis{attraction model}.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 119-120: code points 1-7

\emphasis{Trip Distribution}\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 122-122: code points 1-19

The next stage in the application of the model is to fill the interior of the matrices, “the borders” of which were determined in the previous stage.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 124-125: code points 1-47

This can be seen as a dispute for the places where people will do their daily activities: work, study, shopping, others. At this point we know where people live and when and what activities they participate in based on their personal characteristics (income level, at least); we also know where these activities happen, but not everyone can do them in the same place, and we must assign each person to a location.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 127-130: code points 1-101

This dispute will be based on how people perceive the need to travel as a deterrent to their activities. In the extreme case of a small town where there is no congestion and the longest possible trip is five minutes, trips would be proportionally distributed from every origin to every destination. In a larger city, there will be more trips between closer zones.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 132-135: code points 1-52

Current or future travel times and costs between zones are a function of the existing or proposed transport network supply for the given time of day (public transport frequencies, road network, etc.) and subject to congestion, which is a function of the matrix we are trying to determine (one of many matrices we are trying to determine). Usually a simulation considering a past distribution or simply the distance as cost is used as the start of an iterative process; no more than two reiterations are typically required for reaching a stable solution.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 137-142: code points 1-31

Trips will be distributed with respect to the total cost of travelling from each origin and destination zone, in an attempt to be proportional to users’ perception of the cost of travelling between the zones.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 144-145: code points 1-104

The perception of cost, including the time to wait and transfer, is discussed in Box 4.2. Those perceptions are cultural specific and affects the willingness of a given market segment to travel farther and how they travel. This can be calibrated from the survey data, normally home interviews, as they capture trips of all possible lengths.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 148-151: code points 1-24

This procedure is commonly referred to as the “Gravity Model,” with its mathematical expression to satisfy the fulfillment of the matrix in the above condition given by trips between $i$ and $j$ ($T_{ij}$):\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 153-154: code points 1-113

Eq. 4.1\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 156-156: code points 1-7

Where “\emphasis{Oi” }and\emphasis{ “Dj” }are total trips generated and attracted to zone\emphasis{ i }and\emphasis{ j }respectively, “\emphasis{cij}” is the cost of travelling between \emphasis{i }and \emphasis{j }(generally a combination of time and fares) and “\emphasis{ß” }is a parameter that defines how cost deters travel. The component is usually called a deterrence function, as it expresses the way in which distance (costs) prevents longer trips. As shown in the next figure, where the Y axis indicates how frequently a given trip would be seen for the travel time given in X: the lower the beta calibrated for a given market segment, the less the travel time (or travel generalized cost) is affecting the willingness of that market segment to travel farther to do its activity. The other two parameters, “\emphasis{Ai” }and\emphasis{ “Bj” }are required to ensure the final trip matrix matches the total number of trips generated and attracted at each zone; these are estimated iteratively.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 160-168: code points 1-79

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		\img{\hsize}{operations/assets/45447acb15e17c74fdc76e0a0bde9ab897c56585.png}
		\fignote{37}{How the value of ß affects the deterrent effect of travel costs.\label{chapter:demand-analysis:figure:how-the-value-of--affects-the-deterrent-effect-of-travel-costs}}{Luis Willumsen.}
	}
\fi

\beginbox{2}{Generalized Costs}
\label{chapter:demand-analysis:box:generalized-costs}
The “costs” of using a car or public transport in the transportation models are usually expressed as “generalized costs” that combine time and money elements. A usual formulation for this generalized cost is shown below:\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 174-176: code points 1-15

\highlight{For public transportation:}\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 178-178: code points 1-38

Eq. 4.2\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 180-180: code points 1-7

Where:\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 184-184: code points 1-6

CPT = Costs of using public transport;\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 186-186: code points 1-38

a, b, c, d, e, and f = parameters representing the weight attached to each of these elements in the journey;\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 188-189: code points 1-8

IVT = Time in minutes spent on the bus;\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 191-191: code points 1-39

WTM = Total waiting time to board the bus;\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 193-193: code points 1-42

WAT = Total walking time to and from the bus stop;\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 195-195: code points 1-50

TTM = Time spent transferring from one service to another, if any;\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 197-197: code points 1-66

NTR = Total number of transfers required for the journey, if any;\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 199-199: code points 1-65

FAR = Total fare paid for the whole journey.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 201-201: code points 1-44

The factors\emphasis{ a},\emphasis{ b},\emphasis{ c},\emphasis{ d},\emphasis{ e}, and \emphasis{f }are parameters representing the weight attached to each of these elements in the journey. This generalized cost can be represented in time or monetary units. For example, by dividing the whole formulation by \emphasis{f}, the generalized cost would be measured in money units. It is more advantageous to divide the formulation by \emphasis{a }and then measure generalized costs in (in-vehicle) time units.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 203-207: code points 1-24

Research results agree that walking, waiting, and transfer times are between 1.5 and 3 times more onerous than in-bus times, with the precise values depending on cultural and local conditions like the weather. Similarly, the need to transfer is perceived by customers as adding a notional three to six minutes to their journey. A good starting point is to assume that \emphasis{b},\emphasis{ c},\emphasis{ }and \emphasis{d} to be twice as big as \emphasis{a}, and that \emphasis{e\slash{}a} is about five minutes.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 209-213: code points 1-37

This provisional formulation could then be written as:\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 215-215: code points 1-54

Eq. 4.3\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 217-217: code points 1-7

Where:CPT = Costs of public transport;\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 221-221: code points 1-38

f\slash{}a = Inverse of the value of time savings;\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 223-223: code points 1-43

IVT = Time in minutes spent on the bus;\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 225-225: code points 1-39

WTM = Total waiting time to board the bus;\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 227-227: code points 1-42

WAT = Total walking time to and from the bus stop;\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 229-229: code points 1-50

TTM = Time spent transferring from one service to another, if any;\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 231-231: code points 1-66

NTR = Total number of transfers required for the journey, if any;\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 233-233: code points 1-65

FAR = Total fare paid for the whole journey.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 235-235: code points 1-44

In this case, α is equal to \emphasis{f\slash{}a} and is often interpreted as the inverse of the Value of Time (savings).\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 237-237: code points 1-105

An initial estimate could be the length of working time required to earn one unit of currency, for example how many minutes it takes for the average earner to earn US\$1. The average earner in question is the type of user the new BRT is trying to benefit most. For example, if the average wage rate per hour for the population of interest is US\$2, then \emphasis{α} is thirty minutes per dollar. The generalized costs in this case would be measures in generalized in-bus minutes.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 240-244: code points 1-67

\highlight{For private transport:}\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 246-246: code points 1-34

The formulation would be similar for cars (or motorcycles), but in general it will be assumed that the waiting time is zero, walking is minimal (usually assumed to be zero as well), there will be no transfers, and instead of fares one must consider a combination of fuel and parking costs plus any toll payment.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 248-250: code points 1-101

Eq. 4.4\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 252-252: code points 1-7

Where:\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 256-256: code points 1-6

Ccar = Costs of using a car;\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 258-258: code points 1-28

IVT = Time in minutes spent on the bus;\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 260-260: code points 1-39

FUEL = Cost of fuel;\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 262-262: code points 1-20

Park = Cost of parking;\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 264-264: code points 1-23

a, b, c, d, e, and f = parameters representing the weight attached to each of these elements in the journey;\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 266-267: code points 1-8

TOLL = Cost of tolls.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 269-269: code points 1-21

This implies that car users are only reasonably aware of fuel costs, but ignore maintenance and depreciation costs; there is some evidence that this is the case. For parking, the value of \emphasis{b }is usually assumed to be half of \emphasis{a},\emphasis{ }implying that the parking costs are shared by the onward and return trips. The coefficient \emphasis{c} for toll should be the same as \emphasis{a}, except that paying cash for tolls is usually seen as more onerous than filling up the tank; nevertheless, the default value would be \emphasis{c = a}.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 271-275: code points 1-75

Both expressions for generalized cost (public transport and car) ignore the fact that a car is usually more comfortable and convenient for many journeys. To accommodate this influence, it is customary to add a “penalty” to the less convenient mode, in this case public transport. This penalty is in the range of five to fifteen (generalized) minutes and this extends the generalized cost as:\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 277-280: code points 1-82

Eq. 4.5\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 282-282: code points 1-7

Where:CPT = Costs of public transport;\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 286-286: code points 1-38

IVT = Time in minutes spent on the bus;\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 288-288: code points 1-39

WTM = Total waiting time to board the bus;\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 290-290: code points 1-42

WAT = Total walking time to and from the bus stop;\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 292-292: code points 1-50

NTR = Total number of transfers required for the journey, if any;\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 294-294: code points 1-65

f\slash{}a = Inverse of the value of time savings;\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 296-296: code points 1-43

FAR = Total fare paid for the whole journey.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 298-298: code points 1-44

PENALTY = Cost of public transport being less comfortable and convenient than using a car.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 300-300: code points 1-90

The last two are the versions of generalized costs used in Logit mode choice and Gravity models.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 302-303: code points 1-0

\endbox
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 172-303: code points 1-7

\highlight{Modal Split}\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 305-305: code points 1-23

A complexity not previously mentioned is that costs need to consider private and public transport altogether. When new services are added or taken away, people may shift from one mode to another and some may decide on the least costly as the car.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 307-309: code points 1-31

In summary, the application of this stage consists in further splitting the previous (eventually fifty matrices) in two (adding up to a hundred), one for public transport and one for private transport. A model that includes “Park and Ride” splits the trip into two parts here, including one part in each matrix.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 311-313: code points 1-107

This would entail presenting travellers of a given “market segment” for each OD pair with the times and costs of several possible modes (under the proposed transport network) to see which mode of travel they would choose.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 315-317: code points 1-18

Even inside the same “market segment” the changing decision point is not clear—that is, it is not the same for everyone in that segment. The “answer” of the model is given in proportions, like this: “confronted with the proposed network costs, people travelling from O to D in the given market segment X percent will use public transport (eventually specifying which one), Y percent will use private transport (eventually which one), Z percent will use bicycles.” X, Y, and Z will add up to 100 percent and eventually one (or more) of them will be zero.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 319-324: code points 1-27

The model development consists of analyzing how people make decisions when confronted with various alternatives.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 326-327: code points 1-13

From a policy point of view, perhaps the most important stage in the transport-modeling process is the selection of mode choice for different trips. Determining the number of trips to be made by public transport, nonmotorized options, and private motorized options will have a profound impact on future municipal investments. The factors that affect mode choice can be summarized in three groupings :\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 329-332: code points 1-97

\begin{enumerate}
\item {Characteristics of the trip maker:\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 334-335: code points 9-0

\begin{itemize}
\item Car availability and car ownership;% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 335-336: code points 1-0
\item Possession of driver’s license;% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 336-337: code points 1-0
\item Household structure (young couple, couple with children, retired, single, etc.);% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 337-338: code points 1-0
\item Income;% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 338-339: code points 1-0
\item Residential density.% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 339-339: code points 1-26
\end{itemize}
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 335-339: code points 1-26

}
\item {Characteristics of the journey:\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 340-341: code points 9-0

\begin{itemize}
\item Trip purpose (work, school, shopping, etc.);% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 341-342: code points 1-0
\item Time of day when the journey is taken.% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 342-342: code points 1-44
\end{itemize}
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 341-342: code points 1-44

}
\item {Characteristics of the transport facility:\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 343-344: code points 9-0

\begin{itemize}
\item {Quantitative:\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 344-345: code points 7-0

\begin{itemize}
\item Relative travel time (in-vehicle, waiting, and walking times by each mode);% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 345-346: code points 1-0
\item Relative monetary costs (fares, fuel, and direct costs);% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 346-347: code points 1-0
\item Availability and cost of parking.% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 347-347: code points 1-39
\end{itemize}
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 345-347: code points 1-39

}
\item {Qualitative:\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 348-349: code points 7-0

\begin{itemize}
\item Comfort and convenience;% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 349-350: code points 1-0
\item Reliability and regularity;% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 350-351: code points 1-0
\item Protection and security.% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 351-351: code points 1-30
\end{itemize}
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 349-351: code points 1-30

}
\end{itemize}
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 344-351: code points 1-31

}
\end{enumerate}
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 334-351: code points 1-32

The mode-choice model will typically include these factors in estimating levels of usage between different modes. Segmentation will of course be very important. One should only include choices that are readily available to each type of user. For instance, driving a car is only an option for those in households that own a car. In some cases, travellers with a car provided by their company are in effect captive to that mode, as they have no choice.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 353-357: code points 1-37

If it has been decided that the BRT design must consider customers attracted from other modes, mode-choice modeling will be essential. However, this is a specialized undertaking that usually requires good modeling techniques and trained specialists. If it is not possible to conduct a full modeling process, then it may be appropriate to make a simplified assumption about potential demand increases due to mode shift. This shift is unlikely to represent more than 5 to 20 percent of the demand in the new system.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 359-363: code points 1-94

The most common type of model used to represent mode choice is a logit model (and its similar generalization for multi-class called logit multinomial). The Logit model is a probability distribution for a discrete choice, where the outcome is related to the characteristics of the user that makes a choice. A higher-income transport user, then, will have a higher probability of choosing a private car for his or her trip than a lower income user. This expresses the probability or proportion of trips that would use public transport (Pbus) below instead of cars as:\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 365-370: code points 1-32

Here, the only new element is the parameter "λ." Cbus is the generalized cost for public transportation and Ccar is the generalized cost for a car (this would be calculated for each OD pair). Figure 4.33 shows the influence of this parameter in making choices very dependent on (generalized) cost or less so:\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 374-377: code points 1-30

\ifxetex
	% disabled for now
\else
	{  % group required to avoid fignote settings escaping
		\img{\hsize}{operations/assets/068c588701ad3150e27403bc04e17ccf2f8166f4.png}
		\fignote{38}{The influence of λ on mode choice proportions.\label{chapter:demand-analysis:figure:the-influence-of--on-mode-choice-proportions}}{Luis Willumsen.}
	}
\fi

As can be seen, a high value for λ (0.25) produces a very sharp mode shift for a small difference in costs; a small value (0.02) produces a gentler transition between modes. Both predict a 50\slash{}50 split when the car and bus costs are equal. The value of this parameter must be estimated locally to represent not only local behavior but also the size and nature of zones and networks.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 381-384: code points 1-71

\emphasis{Assignment}\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 386-386: code points 1-12

The previous stages in the modeling process focused primarily on the demand side of public transport services or generating OD matrices. The “assignment” stage is where the supply of public transport services is matched with these demand conditions in a \emphasis{simulation}. This is done by calculating the times and costs required at every path segment of the network, combining it for all the possible paths for all OD pairs to define which will be taken (in which proportion) for each “market segment” (for that given moment of the day).\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 388-393: code points 1-19

Within a BRT system, the assignment stage also helps identify usage levels among different routing and service options. For instance, it is quite useful to know the number of customers who will be utilizing express routes versus local routes.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 395-397: code points 1-35

In order to accurately model public transport route choice, it is necessary to represent the network with a good degree of realism. Near the corridor, many centroids and centroid connectors should be used to better represent access times to stations. Moreover, there is always an additional time to reach the right platform in a BRT or metro system. Transfer times and waiting times for the next available service should also be represented in the generalized cost of travelling along a particular route. People dislike transferring services because of the uncertainty involved, so there is usually a transfer penalty to consider, in addition to the time spent changing services.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 399-405: code points 1-58

Fares should also be accurately represented, and this may prove very tricky in some cases. If there is no fare integration, each change of service will involve paying a new fare. This additional cost may be represented as a “boarding charge.” If the fare has an element proportional to distance, this amount must be added to the journey. For integrated and zonal fares, the issues may be more complex to handle, but most modern software can cope if skillfully used.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 407-411: code points 1-53

It is important to adopt a realistic assignment model for public transport. This is particularly important when dealing with corridors where many bus routes converge. If all bus services have similar operating speeds (a common occurrence on a corridor), earlier models will tend to allocate all trips to the service with the highest frequency. In reality, people will probably choose the first bus that comes along, and thus it is probably best to allocate trips to services based on frequency rather than on an “all-or-nothing” assignment to the highest frequency service. Contemporary software packages, especially those developed and tested for high public transport usage like Emme\slash{}2, Cube\slash{}Trips, VISUM, and TRANUS, perform better in this respect.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 413-420: code points 1-16

Congestion in the system is defined by users’ route choice, and users’ route choice is decided based on the congestion of the system. Equilibrium conditions within assignment are achieved when each customer has been assigned the most efficient route considering the congestion of the system. Equilibrium is very important in dealing with private vehicle assignment, but has an equivalent representation in public transport. Congestion effects may take place because buses are very crowded and users will experience losses in time and comfort (increases in generalized costs), similar to driving under congested conditions. Stopping times increase and customers cannot board a bus (or metro or light-rail vehicle) because it is full, and they must then wait for the next service. Replicating these conditions is important.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 422-429: code points 1-97

For the purpose of designing a new BRT system, excessive crowding and delays to customers because they cannot board a bus should be avoided. Therefore, congested public transport assignment should be less of an issue for design purposes. In any case, congested public transport assignment is tricky and requires good use of a suitable software platform; it should not be attempted without at least a minimum of assisted experience.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 431-435: code points 1-11

\emphasis{Calibration}\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 437-437: code points 1-14

Calibration is the process of, after choosing the (sub) model (i.e., defining how input and output mathematically relate), quantifying (adjust) the parameters that better explain what is observed from the surveys. Calibration is an activity that happens with the development of the model itself. For example, in the models discussed in the previous section, calibration is finding the βs that promote the best fit in the distribution model for each “market segment” or the best λs in the mode selection model.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 439-443: code points 1-99

Usually, when modelers say they are “calibrating the model” they are, in fact, adding detail to the public transport network (eventually calibrating parameters or creating sub-models to generate congestion), so that when they run the full model with present conditions, resulting flows reflect what is seen on the ground (traffic counts and occupancy surveys at that moment of the day). When they calibrate the matrix models, they usually say they are “making the models.”\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 445-449: code points 1-61

\emphasis{Validation}\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 451-451: code points 1-12

Models are developed to represent the reality, based on the isolated observation of several parts in the travel decision-making process. Each simplification is related to an assumption (i.e., everyone in a given market-segment behaves the same). Given the complexity of the travel decision-making process, the simpler the model, the more likely it will not be able to explain and generate details. On the other hand, the higher the aggregation (less detail), the more reliable (the aggregated) results are.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 453-457: code points 1-95

An additional problem is the fact that, in many cases, we do not know the full extent of the reality we are trying to model and cannot obtain that data. For instance, we often do not have the real OD matrix, but the ability to sample the public transport OD is cheaper and is one of the reasons for the reliability of public-transport-only O\slash{}Ds).\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 459-462: code points 1-31

Further, the model is constructed to provide answers (in our case the demand on the BRT system, present and future), the error of the model to forecast answers, and its input requirements and the maintenance of its assumptions define the validity of the model.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 464-466: code points 1-52

A model that does not provide the required accuracy for business proposals may be valid for corridor selection (it can certainly point where more benefit will be generated) and valid for feasibility decisions (it indicates that demand will be above a certain level).\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 468-470: code points 1-57

Validation is then a further requirement in which results from the model are compared with data that has not been used in its construction—for example, a subset of traffic and person counts set aside for this purpose.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 472-474: code points 1-13

Validation further requires that the responses of the model to changes in some inputs, like prices or the introduction of new roads and services, are reasonable and consistent with observations and model results elsewhere.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 476-478: code points 1-18

No model is without errors, but a good calibration and validation process ensures that any significant error is tackled and eliminated and that the model represents the reality of the base-year situation in the best possible way.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 480-482: code points 1-25

A common procedure is to adjust the matrices to match the traffic counts and occupancy surveys; such a procedure, although useful to validate other models for the present, completely undermines the reasoning of the classic four-stage model and severely limits the validity of the generation, distribution, and mode selection models.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 484-487: code points 1-22

\emphasis{Conversion of Demand into Revenue}\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 489-489: code points 1-35

The levels of demand estimated by the models will have to be converted via fares into revenues for the system. This financial modeling will require consideration of the proposed fare system (see Chapter 15: Fare Policy and Structure), the need to share revenue between trunk and feeder services, the existence of certain discounted fares, such as for students and the elderly, and the fact that there will be levels of leakage through fare avoidance and collection loses. For that reason, creating a financial model outside the demand model exercise is needed to understand what the fare should be (see Chapter 14: Financial Modeling). Once the fare is set, though, the demand model should be tested with that determined cost to see how it affects the demand.\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 491-498: code points 1-33

\emphasis{Evaluation}\par
% @ guide/04-demand-analysis/05-estimating-demand-using-a-full-transport-model.src: lines 500-500: code points 1-12

The previous modeling stages have combined supply and demand factors to develop an overall simulation of a city’s public transport services. The final stage of the process is to evaluate the robustness of the particular solution being proposed by the model. The model (and each sub-model) must be plausible, i.e., the proposed relations and parameters that relate input (independent variables) and output (results) make sense (\emphasis{physical sense} in modeler jargon) by changing the output as expected.\par
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Hopefully, the iterations inside the model and in the planning process will converge into a single, identifiable solution for the problem (reducing travel time). If several scenarios produce such a convergence, then the proposed solution is considered to be sufficiently robust. The lack of a convergence may imply that changes in the model structure are necessary before proceeding.\par
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\highlight{Assessment of the Feasibility of the System}\par
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Once some sort of public transport or full transport model has been developed, and a clear scenario for the BRT system has been defined, it should be possible to make a preliminary assessment of the feasibility of the system.\par
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As a proxy for cost savings, the value of time savings can be utilized. However, it should be recognized that time savings is just one of the many reasons for encouraging public transport usage. Other factors include environmental benefits, fuel cost savings, urban design benefits, and social benefits. A more complete feasibility cost analysis would thus include these other factors. Further, time savings may be realized not just by public transport users but by private vehicle users as well.\par
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A good litmus test of whether a new BRT system makes sense is to compare the existing generalized cost of an array of different trips (origin-destination pairs) as they exist before the BRT system, and what the cost might be with the new BRT system serving those trips.\par
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It is important to consider this relationship and to sketch out how a new BRT system would reduce the generalized cost of travel for a set of relevant origin-destination pairs. This calculation can be done using information already available on existing services, fares, frequencies, and travel times, and compared to a new system that may have faster travel times on a trunk corridor, but require transfers and perhaps longer walking times. This would give an idea of how much faster the buses should operate on the trunk corridor to compensate most travellers for the need to add one or more transfers.\par
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For example, consider the introduction of a trunk-and-feeder system that would replace a number of direct services. The following estimates should be made to check whether this scenario is going to improve travel for public transport users. It can be assumed that feeder services will have a similar performance to the current services, but perhaps a higher frequency for the relevant OD pair. It can also be assumed that waiting time will be reduced by two minutes each way, and that walking time will remain the same. The trunk-and-feeder service may require an average of, for example, 1.5 transfers per trip, whereas beforehand there was no transfer. Each transfer will require additional waiting time for the new service (say, two minutes each), so the original savings in waiting time will be lost. The trunk road will have to provide an overall time savings of three times two minutes (six minutes) to be better than the old system, provided that fares remain the same. Therefore, unless one can provide an average time savings on the trunk route of five minutes, it will not be worthwhile to introduce a trunk-and-feeder service. These calculations would have to be repeated for a number of representative journeys to support a decision one way or another.\par
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The existing public transport system may be used to identify some key corridors where significant elements of demand will concentrate. Direct observations of the number of buses, with a reasonable estimation of their customers at peak periods, would enable an initial sizing of the new system. This determination can be achieved in a short period of time and without detailed information on Origin-Destination patterns.\par
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\subsection{3}{Additional Data Needs}
\label{volume:operations:chapter:demand-analysis:section:estimating-demand-using-a-full-transport-model:subSection:additional-data-needs}
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Much of the data required for the full transport demand model will have already been collected during the initial analysis period. It is fairly common for transport departments to do traffic counts, and if recent traffic counts exist in reasonable locations, this data should be usable. If counts for all vehicles were not done earlier, they need to be done now to calibrate the transport model.\par
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Secondly, when the road network is coded into the transport model, it is no longer enough to simply identify existing road links, but the definitions of these links (lanes, width, hierarchy class [ local, arterial], regulated speed, etc.) becomes important. Furthermore, all existing alternative modes such as commuter rail lines, subway lines, bike lanes, and so forth must be coded into the model.\par
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Also, at this point, the demographic and economic activity data for each zone defined earlier becomes important, such as population by zone, employment by zone, average income by zone, vehicle ownership by zone, etc. This information is usually obtained from census data. Historical growth rates in population and employment by zone are the best first indicator of the likely growth rate of future trips in specific locations. Knowing household incomes and motor vehicle ownership levels will help indicate whether most people will take the bus, regardless of the price, or whether they will switch to a car or motorcycle. Mapping the income levels throughout the city will also help define price elasticities and target lower-income beneficiaries, both important to developing the fare structure. Thus, transport models are usually built up from demographic data on population, employment, and vehicle ownership.\par
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Finally, for full transport-demand modeling, the planning team will need to conduct a household and\slash{}or workplace origin-destination survey. This survey is necessary since the team will only have estimates of origins and destinations for public transport trips initially. By contrast, the full-transportation model will require OD matrices for all modes, including walking trips and private-vehicle trips.\par
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Surveying all members of a household regarding individual travel practices (destinations, mode choice, reasons for mode choice, travel expenditures, etc.) provides a very complete picture of where people are going, when, and why. Likewise, workplace surveys can also be an effective mechanism. Unfortunately, household and workplace surveys are probably the costliest of the OD survey techniques. As a result, careful sample sizing is required; knowledge about the variables that will feed the model, their variation, and how they will affect the model output are paramount to the surveys.\par
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Many cities have been led to waste valuable resources with huge samples, in an attempt to obtain a detailed OD matrix\par
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Even with very large sampling, it is certain that the resulting matrix will be very sparse; in other words, most cells will have no trips in them. As a rule of thumb one must have a sample size of 300 to assure that 95 percent of the surveys made will capture events that happen 1 percent of the time. In a 200 zone division, very few OD pairs (out of 40,000 possible OD pairs) would concentrate more than 1 percent of the trips and this means already surveying 60,000 trips [300 hundred for each possible origin zone]). The generation and distribution models are often the best method for matrix estimation with a much smaller sample.\par
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Ortúzar and Willumsen (2011, 80) are quite incisive on this point:\par
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“\emphasis{The first requirement }[knowledge about the variables to be estimated for sample size estimation]\emphasis{, although both obvious and fundamental, has been ignored many times in the past. The majority of household O–D surveys have been designed on the basis of vague objectives, such as ‘to reproduce the travel patterns in the area’. What is the meaning of this? Is it the elements of the O–D matrix which are required, and if this is the case, are they required by purpose mode and time of day, or is it just the flow trends between large zones which are of interest?”}\par
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A good indication that home base OD surveys are being requested without understanding of the basic statistical concepts can be found in many government contracts stating that “the sample for each zone should be enough to guarantee a 5 percent error with 95 percent confidence,” without further stating in relation to what.\par
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In general terms, if no household survey has already been conducted, one would like to collect at least some one thousand home interview surveys, and preferably three thousand, in order to produce a rough four-stage model in the study area. The trip data from these interviews will then be combined with that from intercept surveys to obtain a more accurate trip pattern in the study area.\par
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\section{6}{Risk and Uncertainty}
\label{volume:operations:chapter:demand-analysis:section:risk-and-uncertainty}
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\quotation{Doubt is uncomfortable, but certainty is absurd.}{Voltaire, writer, historian, and philosopher, 1694–1778}
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However sophisticated, a model of transport demand is still a model, a simplified representation of a real-world scenario. A degree of uncertainty will always remain in any travel-demand forecast, and BRT planners should keep this in mind.\par
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In terms of demand forecasting, there are two main sources of error:\par
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\begin{enumerate}
\item Will the variables that define the future context (population, income, location, employment, etc.) and policy environment (car restraint, competition, pricing, and subsidies) for the BRT adopt the values forecast at the planning stage?% @ guide/04-demand-analysis/06-risk-and-uncertainty.src: lines 11-12: code points 1-0
\item Do the models capture true travel behavior, and will the travel preferences identified (coefficients in the generalized cost formulation and the different sub-models) remain in the future?% @ guide/04-demand-analysis/06-risk-and-uncertainty.src: lines 12-12: code points 1-196
\end{enumerate}
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The best way to handle these sources of uncertainty is to develop forecasts under different scenarios. In the worst circumstances, more-dispersed urban growth will continue to incentivize the use of conventional buses, taxis, and minibuses and compete, to some extent, with the BRT system, and that no car-reduction policy will be implemented. In the best-case scenario, urban growth will be focused on BRT, car-reduction policies will be implemented, competing modes will be kept some distance away from the BRT trunk network, and they will not be subsidized. An expected or probable scenario would assume a partial implementation of those policies. The definition of these scenarios will have to be agreed upon by all stakeholders, with some consultation of possible bidders and financial institutions.\par
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\ifxetex
	% disabled for now
\else
	{  % group required to avoid fignote settings escaping
		\img{\hsize}{operations/assets/f4d705e659ca006211bcfaccbe7c9904034698dc.png}
		\fignote{39}{An illustration of how these different scenarios could be presented.\label{chapter:demand-analysis:figure:an-illustration-of-how-these-different-scenarios-could-be-presented}}{Luis Willumsen.}
	}
\fi

Uncertainties about capturing the true travel behavior within the model can be treated in different ways. While it is difficult for a model to accurately capture travel behavior, the basis for the model will be the demand on the existing routes that currently run on the corridor. Those existing routes and their combination of services are reasonably predictable. Ideally, that demand will have transferred to the new BRT corridor if those previous services are no longer allowed to run on the corridor and that should form the basis of any forecast. This transferred demand will tend to grow with population, although this is perhaps less certain.\par
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Other components of BRT demand will be those captured from semi-competitive modes not fully removed from competition: taxis and shared taxis. Other demand may be abstracted from cars if the BRT service is good enough and car restraint policies are implemented. Finally, some people may choose to change the destination of their trips, perhaps for shopping or entertainment, to take advantage of the better accessibility offered by BRT.\par
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These sources of demand have been outlined in increasing degrees of uncertainty or confidence in our ability to model them accurately enough. It is desirable, therefore, to present the final demand estimations and deconstruct the individual components or contributions. This is illustrated in Figure 4.35. In this way, private and public sector stakeholders, concerned about the sources of risk in the project, can understand the most solid basis for demand and revenue projections.\par
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\ifxetex
	% disabled for now
\else
	{  % group required to avoid fignote settings escaping
		\img{\hsize}{operations/assets/7700a9a925fffc05614bcefad1b8cdaf98933845.png}
		\fignote{40}{Decomposition of BRT demand estimates.\label{chapter:demand-analysis:figure:decomposition-of-brt-demand-estimates}}{Luis Willumsen.}
	}
\fi

Finally, some stakeholders, particularly in the financial community, advocate the use of stochastic simulations to address the issue of uncertainty in forecasts. In this case, the analysis involves the use of Monte Carlo simulations usually implemented as an add-on to a standard spreadsheet.\par
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The first step is to agree with stakeholders on the few input or model variables that will be considered stochastic rather than fixed, and relate the outputs from the model to the stakeholders. Additional model runs will be needed to identify, for example, how variations in GDP growth affect revenues and therefore car ownership. This requires exercising the model in sensitivity tests, using different values of time in mode choice.\par
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The next step would be to adopt some probabilistic distribution around the mean expected values of these variables. It is common to assume that these would be independent normal distributions, although this assumption was partly to blame in the risk models before the 2008 financial crisis.\par
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The next step is to construct a model where this handful of variables affects demand, and where their probabilistic distributions are sampled repeatedly in a Monte Carlo simulation. Each run of a Monte Carlo simulation reflects one possible demand and revenue path diverging from the expected scenario. This is illustrated in Figure 14.36, where each path represents a diversion from the expected case normalized to 1; a revenue factor value of 0.95 in one year implies that collections in that case would be only 95 percent of the expected case for that year.\par
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\ifxetex
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	{  % group required to avoid fignote settings escaping
		\img{\hsize}{operations/assets/d4745c55f9b3b0d25d338343f871c74bb4a938ac.png}
		\fignote{41}{Possible revenue factors in a stochastic simulation.\label{chapter:demand-analysis:figure:possible-revenue-factors-in-a-stochastic-simulation}}{Steer Davies Gleave.}
	}
\fi

These results can be aggregated in different ways to express the probability that a particular level of demand (and revenue) will be exceeded, say, 90 percent of the time. This is usually referred to as the P90 forecast and it is sometimes used to finance private sector schemes.\par
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The value of this treatment is limited in the case of BRT schemes where uncertainty resides more in transport and land use policy that is better treated through scenario analysis.\par
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\section{7}{Conclusion}
\label{volume:operations:chapter:demand-analysis:section:conclusion}
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With either demand on the corridor from the rapid assessment model or through the transport model, many critical design decisions can be made with a reasonable degree of accuracy. This demand estimation forms the foundation for service planning (Chapter 6: Service Planning) to financial modeling (Chapter 14: Financial Modeling) to infrastructure planning, including station size for buses and customers, passing lanes, etc. (Volume 6: Infrastructure, Chapters 21–27).\par
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BRT stations need to be sized in order not to saturate, and avoiding saturation requires designing stations to a specific volume of boarding and alighting customers and bus frequencies. Cities should establish their goals for percentage of overall mode share by bus-based public transport, and make projections accordingly with regard to future growth (and not just design based on existing demand). This approach has been taken to calculate BRT station sizes in Pimpri Chinchwad, India, where the region has set a goal of reaching 60 percent of mode share by bus-based public transport by 2013. The formulas for avoiding station saturation are included in Chapters 6: Service Planning and 7: Capacity and Speed.\par
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With the data on the maximum load at the critical link, many other preliminary judgments can be made about the basic system design. For instance, if the number of customers on BRT buses at the critical link is above 7,000 passengers per hour per direction (pphpd), then a simple single-lane, single-route BRT system may not be able to handle the demand. If the demand is below 2,000 passengers per direction during the peak hour, BRT measures may not be very cost effective unless significant new land development is planned in the area or a high level of modal shift is likely due to very low mixed traffic speeds. If the demand is greater than 36,000 pphpd, it may be better to split demand onto multiple parallel arterials or consider grade separation.\par
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Furthermore, using simple calculations about speed and route length, one can also begin to settle other issues like the size of the needed new bus fleet, and the appropriate size of the bus. The formulas for performing these calculations, once a basic calculation of the maximum load on the critical link has been made, are included in Chapter 6: Service Planning.\par
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\highlight{Bibliography}\par
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Bruton, Michael J. \emphasis{Introduction to Transportation Planning}. London: Hutchinson, 1985.\par
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Ortúzar, Juan, and Luis G. Willumsen. \emphasis{Modelling Transport}. 4th edition. UK: Wiley, 2011.\par
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Steer Davies Gleave. “Estimation of private vehicle trips pelaced by TransMilenio: Phasie II report.” Bogotá, 2003.\par
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