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\chapter{7}{System Speed and Capacity}
\label{volume:operations:chapter:system-speed-and-capacity}
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Designing a BRT system to comfortably handle high customer demand in a rapid manner is paramount to delivering a car-competitive service. The cities of Bogotá, Colombia; Rio de Janeiro, Brazil; Belo Horizonte, Brazil; Istanbul, Turkey; Guangzhou, China; and Brisbane, Australia, have firmly demonstrated that high-speed and high-capacity operations can be achieved with BRT at a considerably lower cost than rail options, while providing shorter distances and shorter access times to destinations than rail options.\par
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Although accessibility, comfort, communication, cost, capacity, speed, and frequency are all noticeable features of successful BRT systems, speed sets BRT apart from conventional bus systems. Once detailed service planning is in place, infrastructure and technology specifications must be made in order to guarantee that the system can perform as required to deliver sufficient speeds.\par
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This chapter explains how the basic features of the BRT system and the details of the station’s specifications affect the capacity of the system, and how to make the right decisions to guarantee that the system will maintain high speeds for any given demand forecast. The chapter explores the reasoning behind the procedures used in Chapter 6: Service Planning to show that the planning process is simple; difficulty arises only when there are too many variables. If data is properly organized, the optimum results are achievable.\par
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The first section revisits the goals of the design; the second section outlines the process of evaluating corridor capacity and speed; the third section focuses on how increasing capacity affects speed through the concept of station saturation; and the fourth section provides the equation for calculating speeds and capacity for a proposed solution. The last two sections consider the effects of basic features on system capacity and speed.\par
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\section{1}{Design Objectives}
\label{volume:operations:chapter:system-speed-and-capacity:section:design-objectives}
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\quotation{An optimist will tell you the glass is half-full; the pessimist, half-empty; and the engineer will tell you the glass is twice the size it needs to be.}{Anonymous}
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BRT projects seek to optimize one basic parameter: minimizing door-to-door travel time. Thus, to achieve that goal, BRT must be designed and planned—including corridor selection, routes and service plan, and alternatives—to ensure that:\par
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\begin{itemize}
\item Capacity is sufficient to handle expected customer demand;% @ guide/07-system-speed/design-objectives.src: lines 9-10: code points 1-0
\item Waiting and transfer times are reduced by providing frequent service;% @ guide/07-system-speed/design-objectives.src: lines 10-11: code points 1-0
\item Speeds are high, because of reduced delay at stations; ideally, average speeds should be above 20 kph in areas with frequent intersections, 25 kph where there is little mixed traffic interference, and above 40 kph for limited-stop services.% @ guide/07-system-speed/design-objectives.src: lines 11-13: code points 1-43
\end{itemize}
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The reduction of waiting\slash{}transfer times should have been addressed with service planning. The focus of this chapter is how to achieve sufficient capacity and high speeds. As the number of vehicles and customers increases, the opportunity for bottlenecks and operational problems multiplies. Busway systems can be designed to operate at high capacities, but in some cases, high-demand designs have also produced relatively slow commercial speeds. For example, prior to the Bogotá TransMilenio system, the busway on the Avenida Caracas corridor was able to move more than 30,000 passengers per hour per direction (pphpd); this is less than the current BRT system in the same corridor, but it still exhibits a very high demand. However, due to significant congestion, the vehicles only averaged 10 kph. By comparison, the TransMilenio BRT system operates at an average commercial speed of approximately 27 kph.\par
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Assuming that congestion in mixed traffic is ruled out with the creation of a busway, two obvious candidates can cause delay in the BRT system: intersections and stations. Unless there is conflict between station and intersection design, one should assume that, at intersections, buses would experience a delay similar to cars. One can expect that the delay experienced by the BRT at intersections is similar to cars in off-peak hours and that BRT will perform better than cars at rush hour. Chapter 24: Intersections and Traffic Signals addresses station location in relation to intersection design, reviews station delays associated with intersections, and suggests how to improve BRT and car travel times at intersections. \emphasis{If stations are properly designed, they are far more likely to increase BRT speeds.} If not properly designed, however, stations may contribute to decreased speeds, as well as decreased capacity in relation to a conventional system.\par
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		\img{\hsize}{operations/assets/5b7ba08abd0b7e4480f9ea8d89d101b7c609d4d9.jpeg}
		\fignote{1}{Designing a system along corridors with demands exceeding 30,000 passengers per hour per direction, like the one in Bogotá is quite different from designing systems with less than 3,000 pphpd, like the one shown in figure 7.2\label{chapter:system-speed-and-capacity:figure:designing-a-system-along-corridors-with-demands-exceeding-30000-passengers-per-hour-per-direction-like-the-one-in-bogot-is-quite-different-from-designing-systems-with-less-than-3000-pphpd-like-the-one-shown-in-figure-72}}{Carlos F. Pardo.}
	}
\fi

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		\img{\hsize}{operations/assets/50b78656b8db49946bf446c15ce6052c6900f8dd.jpeg}
		\fignote{2}{System to serve less than 3,000 pphd in Orlando (Florida, USA), is quite different from system to serve 30.000 ppphpd as the shown in figure 7.1\label{chapter:system-speed-and-capacity:figure:system-to-serve-less-than-3000-pphd-in-orlando-florida-usa-is-quite-different-from-system-to-serve-30000-ppphpd-as-the-shown-in-figure-71}}{Lloyd Wright.}
	}
\fi

The demand analysis process and demand forecast modelling can help quantify existing public transport demand, as well as provide projections of expected demand and growth. A system’s infrastructure should be designed for capacities projected at least one decade into the future. The size of the growth cushion will depend on how fast a city’s population and mobility needs are increasing. For example, in some rapidly urbanizing Chinese cities, growth rates of up to 25 percent are being realized in fewer than five years. In such instances, a growth cushion of 50 percent or higher may be appropriate for sizing the system’s capacity. In other regions that are already highly urbanized, such as Latin America, growth rates are much lower. In Latin American cities, a growth cushion of 25 percent has been used.\par
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Designing a high-capacity and high-speed BRT system does not guarantee that door-to-door travel times will be minimized. High-capacity and high-speed services can be achieved simply by eliminating all the stops along a BRT corridor, and having service run only between the two terminals. Metro systems are often designed with very long distances between station stops in order to increase average speeds and capacity. However, this has an adverse impact on door-to-door travel times, as customers will have much farther to walk to reach the nearest public transport station. The system’s design therefore must be optimized so as to minimize door-to-door travel times for the majority of customers—not just in terms of speed and capacity.\par
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The specific design solutions to achieve high capacity vary widely for different levels of demand. For example, a BRT system that only needs to handle a demand of 5,000 passengers per hour per direction (pphpd) will be significantly different from a system requiring over 30,000 pphpd (Figures 7.1 and 7.2).\par
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\section{2}{The Process of Designing High-Capacity and High-Speed BRT Corridors}
\label{volume:operations:chapter:system-speed-and-capacity:section:the-process-of-designing-high-capacity-and-high-speed-brt-corridors}
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\quotation{Design is the conscious effort to impose a meaningful order.}{Victor Papanek, designer and educator, 1927–1998}
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To clearly understand the following three sections, the reader must be familiar with the definitions outlined in the “Basic Concepts” section of Chapter 6: Service Planning.\par
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\subsubsection{1}{Concepts Review}
\label{volume:operations:chapter:system-speed-and-capacity:section:the-process-of-designing-high-capacity-and-high-speed-brt-corridors:subSection::subSubSection:concepts-review}
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One can reasonably expect that capacities are independent of demand — that once the specifications of design and operation are given, the capacity could be calculated regardless of the demand. This is true for each feature of BRT, but there are so many possible limitations to these features that a global analysis without restricting them to the intended use has no practical purpose. Consider the analogy of a truck’s capacity (or a glass mug’s, as in Figure 7.3): maximum volume, maximum weight, and maximum dimensions are clearly defined, but discussing capacity without knowing what will be transported — cotton, steel, or windmill blades—leaves too many possibilities open. Similarly, given an elevator design — load, speed, door opening times, and protocol to answer calls — one can simulate the wait and travel times for a given demand pattern, but to evaluate all of them would be impractical. It is much easier to evaluate the capacity for a given use.\par
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		\img{\hsize}{operations/assets/6a3b1ec635e3933e56c8d19a53218fe4774120bd.jpeg}
		\fignote{3}{Analyzing capacity without use assumptions leaves too many possibilities open.\label{chapter:system-speed-and-capacity:figure:analyzing-capacity-without-use-assumptions-leaves-too-many-possibilities-open}}{Arthur Szász.}
	}
\fi

For this edition, we changed the approach for calculating operational capacity of a corridor: \emphasis{the capacity of the corridor is the capacity of its bottleneck} One must evaluate the capacity of each station and traffic lights under predicted conditions to check the corridor capacity.\par
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In order to compare situations for design, \emphasis{capacity} is defined as the number of customers that \emphasis{can} pass through a given cross section during a time interval. For practical purposes, we will examine that cross section for one-way traffic only and use one hour as a reference measure for the interval.\par
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The \emphasis{capacity of a segment} (or link) should be given by the lower capacity of a section within it (lower capacity section along the segment). Unlike intersections, where the stop line can be readily identified as the bottleneck of the approaching segment, BRT stations with multiple sub-stops are harder to model as single session; they are easier seen as segments which will have still many potential sections to evaluate for discovering the lower-capacity section. Anyway, the capacity is the maximum possible load across the station.\par
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Further confusion could arise by considering the station as a segment, because the load entering the station is not the load leaving the station, unlike a traditional demand segment or link in modelling that is between stations. But that is not a problem for calculating capacity because we are considering the maximum possible load. When dealing with demand load, every demand load in the system would still be taken into account because the load that leaves one station arrives at the next. For the purposes of calculating capacity here, the demand load of a station is the demand load that arrives at it.\par
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As mentioned before and as will be highlighted many times in the following discussions and examples, high capacity is achievable at the cost of speed. Therefore, in agreement with our design objectives, we define the \highlight{operational capacity} as \highlight{the capacity that does not cause bus queues at the stations}.\par
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What constitutes a queue is still a bit subjective and will be discussed when queue formation is evaluated. One should notice that having no queue does not necessarily imply reduced time at the station. For example, one station in a well-designed system could have a boarding and alighting process taking one minute regularly every five minutes (20 customers boarding through a stepped door), and that would be a fine operation with low operational capacity, which will also be shown later in the chapter.\par
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This definition of capacity is not the capacity of the station itself, but the capacity of the corridor segment where the station is. The station itself has other capacities that need to be specified (or calculated for a given design), including:\par
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\begin{itemize}
\item Number of waiting customers, which varies based on comfort parameters;% @ guide/07-system-speed/the-process-of-designing-high-capacity-and-high-speed-brt-corridors.src: lines 60-61: code points 1-0
\item Number of boardings per hour and number of alightings per hour, which may be given by a curve when one is dependent of the other (i.e., they use the same door).% @ guide/07-system-speed/the-process-of-designing-high-capacity-and-high-speed-brt-corridors.src: lines 61-62: code points 1-66
\end{itemize}
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More information on station design can be found in Chapter 25: Stations.\par
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\emphasis{The renovation rate} is an important concept for understanding that the total number of customers transported by a route may be higher than the capacity of the route as usually defined (it can be lower, too, but that is not difficult to understand). The same concept is needed to evaluate the BRT corridor: the number of customers using the system may be higher than its capacity.\par
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Another observation must be made regarding the renovation rate: when operators and business analysts mention it, they normally refer to transported customers (in a trip, route, or corridor) over the effective supply (the capacity of the vehicles providing the service—trip-places), ignoring both the load factor required for appropriate planning and the fact that in the first years of operation the supply is often lower than capacity, because corridors need to be designed for higher capacity than existing demand, and thus, supply.\par
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One must keep in mind that transport systems’ speeds, waiting times, supplied trip-places, demand, and maximum loads will change throughout the day, throughout the seasons, and throughout the years. By monitoring operational data of a built system, one can certainly tell the lower bounds of its capacity (capacity will certainly be greater or equal to the maximum load observed), but even so, the operational capacity would be subject to debate.\par
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\subsubsection{2}{Simulating Solutions}
\label{volume:operations:chapter:system-speed-and-capacity:section:the-process-of-designing-high-capacity-and-high-speed-brt-corridors:subSection::subSubSection:simulating-solutions}
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Regardless of the debate about operational capacities of transport systems, it is very simple to evaluate the capacity and speed of a proposed design solution under any condition and compare it with other solutions and conditions, including using a car. Besides understanding how to compute dwell time and queuing time in stations, which will be discussed in this chapter, and at traffic lights, which will be discussed in Chapter 24: Intersections and Traffic Signals, the basic requirement is manipulation of the cinematic concept of speed.\par
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Speed is useful to bringing users travelling different distances to a common location, but for evaluation of the system, only the use of speed for evaluation is necessary because people, including planners in and stakeholders of the project, are familiar with speeds, knowing the sensation of movement associated with the readings in a speedometer of a car. A planner compares alternatives of the same implementation and operational cost by comparing the sum of travel times for all people in the city (both those using and not using the system) with the implementation of each alternative.\par
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That said, the equations needed to simulate and compare speeds are:\par
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$ \text{average speed} ={\text{travel distance} \over \text{travel time} } \leftrightarrow \text{travel time} = {\text{travel distance} \over \text{average speed}} $\par
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$ \text{travel time}_{A-Z} = \text{travel time}_{A-M} + \text{wait time}_M + \text{travel time}_{M-Z} $\par
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The last equation assumes point M is part of travelling itinerary between points A and Z. It can be further divided, assuming that point B is between points A and M.\par
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$ \text{travel time}_{A-M} = \text{travel time}_{A-B} + \text{wait time}_B + \text{travel time}_{B-M} $\par
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This implies the obvious understanding that the travel time along a corridor is the sum of travel time in every segment (or link) with the wait time at every stopping point.\par
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A simple table like Table 7.1 can be constructed to compare travel time and speed for different alternatives and conditions.\par
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					\tabletitle{1}{Example of Travel Time Simulation under Different Conditions}
\label{chapter:system-speed-and-capacity:table:example-of-travel-time-simulation-under-different-conditions}

					\img{\hsize}{operations/assets/689b826a166f0b9c2c7a5617d1f63dfef3f646ce.png}
				}
			\fi

This table can be placed in a different order, joining the unshaded rows (segment times), green shaded rows (intersection times), the gray shaded rows (station times) and programmed to calculate each value based on the characteristics of the proposed design and operation based on the following formulas of this chapter for stations, the formulas given in Chapter 24: Intersections and Signal Control, and in the basic speed formula given for segments. In the initial planning phases, assumptions for intersections plus segments based on observed travel times are good enough for a practical evaluation of alternatives.\par
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A similar table (as shown in Table 7.1) can be made for the capacity of each section, once formulas for capacity are given for stations in the remainder of the chapter, and formulas for capacity of segments, and intersections are given in Chapter 24, based on the proposed design characteristics.\par
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So, with the forecast demand and the design, one can tell if the required performance, initially taken as an assumption, is reached. If not, modifications need to be proposed for the infrastructure (or technology) where failure was observed in relation to expected results. In the unlikely case that an excessive performance is seen, adjustments could also be proposed for a more adequate system. For both cases, the last two sections of this chapter (7.5 and 7.6) will discuss how station, vehicle, and operation features affect speed and capacity.\par
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The next section (7.3) explains how the station design associated with the service plan affects system speed and the following (Section 7.4) discusses how the design of the station for a given boarding and alighting demand affects system capacity.\par
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\section{3}{Understanding Station Saturation}
\label{volume:operations:chapter:system-speed-and-capacity:section:understanding-station-saturation}
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\quotation{How did it get so late so soon? It’s night before it’s afternoon. December is here before it’s June. My goodness how the time has flewn. How did it get so late so soon?}{Theodor Seuss Geisel (aka “Dr. Seuss”), writer and cartoonist, 1904–1991}
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If the highest frequency at a BRT corridor bottleneck is one bus per minute, the rest of the corridor will have at most a one bus per minute frequency. Identifying this weak link in the system is the foundation for improving capacity and avoiding congestion, which in turn improves travel times. In general, the critical factor on a public transport system is\highlight{ }\emphasis{vehicle congestion at the station}.\par
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The fact that BRT systems are now able to reach speeds and capacities comparable to all but the highest capacity metro systems is due to dramatic improvements in vehicle capacity at stations. Other factors are also important to reaching these speed and capacity goals, but none are as important as preventing docking bay congestion. Many existing BRT systems are burdened with slow operating speeds due to incorrect demand projections at particular stations. Poorly designed stations can lead to peak-hour vehicle queues that stretch for several hundred meters.\par
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The proper design of a station to prevent congestion is directly related to the concept of \emphasis{saturation level}, usually referred to simply as \emphasis{saturation}.\par
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The \emphasis{saturation level} of a docking bay refers to the proportion of time that a vehicle docking bay is occupied. A docking bay is the designated area in a BRT station where a vehicle will stop and align itself to the boarding platform.\par
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A docking bay might be considered occupied at any given moment, if a bus that wants to position itself at that docking bay cannot proceed with the proper maneuver. Therefore, a vehicle is “using” the docking bay from the moment its approach will prevent other vehicles from doing the same thing until the moment it leaves the area so that another vehicle can approach. The amount of time that any given vehicle occupies a docking bay is called dwell time ($T_d$).\par
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\beginbox{1}{Sub-Stops and Docking Bays}
\label{chapter:system-speed-and-capacity:box:sub-stops-and-docking-bays}
A schematic view of a station, from \emphasis{The} \emphasis{BRT Standard}, shows the components of a station including the sub-stops and the docking bays, defined as follows:\par
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\begin{itemize}
\item \emphasis{Sub-stop}: a station subdivision or “module” that includes docking bays and customer circulation and waiting areas. One station can have multiple sub-stops that are physically connected. In order for multiple sub-stops to function, they typically need overtaking lanes or a functional equivalent such as in the “directional” BRT stations used in Lanzhou and Yichang.% @ guide/07-system-speed/understanding-station-saturation.src: lines 35-39: code points 1-0
\item \emphasis{Docking bay}: the platform-vehicle interface; this is where the bus pulls up in order to let customers on and off.% @ guide/07-system-speed/understanding-station-saturation.src: lines 39-40: code points 1-21
\end{itemize}
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		\img{\hsize}{operations/assets/746f742be80b4df15d131e23a740b9e6f922c9ec.jpeg}
		\fignote{4}{Scheme of station with two substops\label{chapter:system-speed-and-capacity:figure:scheme-of-station-with-two-substops}}{ITDP}
	}
\fi

\endbox
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Therefore, the saturation of a docking bay ($ x_dock $) measured for a certain time interval ($ \Delta t $) would be given by the sum of dwell times of each bus docking ($ Ti di $) during that interval, divided by the duration of the interval, as expressed by Equation 7.1.\par
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Eq. 7.1 Saturation of a docking bay concept:\par
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$ x_\text{dock}  = {\sum_{\text{docking bus} i \in \Delta t} {T}_{i d} \over \Delta t} $\par
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Where:\par
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\begin{itemize}
\item $ x_\text{dock}$: Saturation of a docking bay;% @ guide/07-system-speed/understanding-station-saturation.src: lines 56-57: code points 1-0
\item $ \sum_{\text{docking bus} i \in \Delta t} {T}_{i d} $: Sum of docking times of buses;% @ guide/07-system-speed/understanding-station-saturation.src: lines 57-58: code points 1-0
\item $ \Delta t$: Time interval.% @ guide/07-system-speed/understanding-station-saturation.src: lines 58-58: code points 1-35
\end{itemize}
% @ guide/07-system-speed/understanding-station-saturation.src: lines 56-58: code points 1-35

Calling $T_d$ the average dwell time (which is equivalent to assuming that all buses will have the same dwell time $T_d$), saturation would be:\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 60-60: code points 1-147

$ x_\text{dock}  = {N_{\text{buses} \in \Delta t}  * {T}_{i d} \over \Delta t} $\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 62-62: code points 1-82

And calculating the saturation for one hour would result in the following equation:\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 64-64: code points 1-83

Eq. 7.2 Saturation of a docking bay for one hour:\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 66-66: code points 1-49

$ x_\text{dock}  = {Freq_\text{dock} * T_d  \over 3600\text{sec}} $\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 68-68: code points 1-69

Where:\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 70-70: code points 1-6

\begin{itemize}
\item $ x_\text{dock} $: Saturation of a docking bay;% @ guide/07-system-speed/understanding-station-saturation.src: lines 72-73: code points 1-0
\item $ \text{Freq}_\text{dock} $: Frequency of buses docking;% @ guide/07-system-speed/understanding-station-saturation.src: lines 73-74: code points 1-0
\item $ T_d $: Dwell time.% @ guide/07-system-speed/understanding-station-saturation.src: lines 74-74: code points 1-28
\end{itemize}
% @ guide/07-system-speed/understanding-station-saturation.src: lines 72-74: code points 1-28

The dwell time consists of three separate categories: boarding time, alighting time, and dead time. Boarding and alighting times can partially happen simultaneously, depending on the system protocol for boarding and alighting. Dead time is the time consumed by a vehicle slowing down when approaching a station, opening its doors (and after a variable dwell time for boarding and alighting), closing its doors, and then regaining free flow speed. It is also called \emphasis{fixed dwell time} because it is the part of dwell time that does not vary with the number of customers boarding and alighting at each stop, and it usually does not vary between stops in a system.\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 76-82: code points 1-40

Two elements of the BRT vehicle affect dead time: the rate of vehicle deceleration when approaching a stopping bay and the rate of acceleration when departing from a docking bay. Besides the vehicle capabilities (power-to-weight and gear relations), the deceleration and acceleration rates often involve a trade-off between speed and customer comfort, as well as the ability to properly align the vehicle to the stopping-bay interface.\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 84-88: code points 1-10

Vehicle size also affects the dead time. In non-congested situations, vehicles typically require ten to sixteen seconds to open and close their doors and pull in and out of a station and, when in a queue, up to twenty seconds. However, larger vehicles require more time, and additional time between 0.17 to 0.25 seconds per meter of vehicle is generally required for pulling in and out of the station. For a more detailed model, these values need to be calibrated to produce the amount of dead time, but as a conservative approach Equation 7.3 can be used.\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 90-95: code points 1-47

Eq. 7.3 Impact of vehicle length on dwell time without queue\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 97-97: code points 1-60

$ T_0 = 13 + L_\text{vehicle} * 0.25 $\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 99-99: code points 1-40

Where:\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 101-101: code points 1-6

\begin{itemize}
\item $ T_0$: Dead time in seconds;% @ guide/07-system-speed/understanding-station-saturation.src: lines 103-104: code points 1-0
\item $ L_\text{vehicle}$: Length of the vehicle in meters.% @ guide/07-system-speed/understanding-station-saturation.src: lines 104-104: code points 1-61
\end{itemize}
% @ guide/07-system-speed/understanding-station-saturation.src: lines 103-104: code points 1-61

As for the variable part of dwell time (boarding and alighting time), the main factors affecting it include:\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 106-108: code points 1-0

\begin{itemize}
\item Customer-flow volumes;% @ guide/07-system-speed/understanding-station-saturation.src: lines 108-109: code points 1-0
\item Number of vehicle doorways;% @ guide/07-system-speed/understanding-station-saturation.src: lines 109-110: code points 1-0
\item Width of vehicle doorways;% @ guide/07-system-speed/understanding-station-saturation.src: lines 110-111: code points 1-0
\item Entry characteristics, e.g., stepped or at-level entry, using all doors or having specific doors to board and alight;% @ guide/07-system-speed/understanding-station-saturation.src: lines 111-113: code points 1-0
\item Open space near doorways (on both vehicle and station sides);% @ guide/07-system-speed/understanding-station-saturation.src: lines 113-114: code points 1-0
\item Fare collection position, e.g., at the station entrance, at the bus entrance, in the middle of the bus;% @ guide/07-system-speed/understanding-station-saturation.src: lines 114-116: code points 1-0
\item Doorway control system.% @ guide/07-system-speed/understanding-station-saturation.src: lines 116-116: code points 1-29
\end{itemize}
% @ guide/07-system-speed/understanding-station-saturation.src: lines 108-116: code points 1-29

BRT systems are able to operate metro-like service in large part due to the ability to reduce total stop time to twenty seconds or less. A conventional bus service often requires more than sixty seconds for stop time, though the specific time will be a function of the number of customers and other factors. In general, dwell times may be somewhat higher during peak periods than nonpeak periods. The increase during peak periods is due to the additional time needed to board and alight the higher customer volumes.\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 118-122: code points 1-105

In a system where both boarding and alighting happens through all doors, dwell time is given by Equation 7.4:\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 124-125: code points 1-4

Eq. 7.4 Dwell time for one bus with an external fare collection and all-door boarding: $ T_d =T_0 + p_b * t_b + p_a * t_a $\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 127-128: code points 1-38

Where:\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 130-131: code points 1-0

\begin{itemize}
\item $ T_d $: Dwell time for a given bus;% @ guide/07-system-speed/understanding-station-saturation.src: lines 131-132: code points 1-0
\item $ T_0 $: Dead time for the given bus;% @ guide/07-system-speed/understanding-station-saturation.src: lines 132-133: code points 1-0
\item $ t_b $: Average boarding time per customer;% @ guide/07-system-speed/understanding-station-saturation.src: lines 133-134: code points 1-0
\item $ t_a $: Average alighting time per customer;% @ guide/07-system-speed/understanding-station-saturation.src: lines 134-135: code points 1-0
\item $ p_b $: Number of boarding customers at the given bus;% @ guide/07-system-speed/understanding-station-saturation.src: lines 135-136: code points 1-0
\item $ p_a $: Number of alighting customers at the given bus.% @ guide/07-system-speed/understanding-station-saturation.src: lines 136-136: code points 1-64
\end{itemize}
% @ guide/07-system-speed/understanding-station-saturation.src: lines 131-136: code points 1-64

If boarding is made exclusively through different doors than alighting, then the dwell time for a bus is given by Equation 7.5:\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 138-139: code points 1-25

Eq. 7.5 Dwell time for one bus with specific doors for boarding or alighting:\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 141-141: code points 1-77

$ T_d = T_0 + Max(p_b *t_b,p_a * t_a) $\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 143-143: code points 1-41

Where:\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 146-147: code points 1-0

\begin{itemize}
\item $ T_d $: Dwell time for a given bus;% @ guide/07-system-speed/understanding-station-saturation.src: lines 147-148: code points 1-0
\item $ T_0 $: Dead time for the given bus;% @ guide/07-system-speed/understanding-station-saturation.src: lines 148-149: code points 1-0
\item $ t_b $: Average boarding time per customer;% @ guide/07-system-speed/understanding-station-saturation.src: lines 149-150: code points 1-0
\item $ t_a $: Average alighting time per customer;% @ guide/07-system-speed/understanding-station-saturation.src: lines 150-151: code points 1-0
\item $ p_b $: Number of boarding customers at the given bus;% @ guide/07-system-speed/understanding-station-saturation.src: lines 151-152: code points 1-0
\item $ p_a $: Number of alighting customers at the given bus.% @ guide/07-system-speed/understanding-station-saturation.src: lines 152-152: code points 1-64
\end{itemize}
% @ guide/07-system-speed/understanding-station-saturation.src: lines 147-152: code points 1-64

Equation 7.6 shows the saturation calculation for the situation where both boarding and alighting occur through the same doors.\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 154-155: code points 1-23

Eq. 7.6 Calculating the saturation level of a docking bay for a given time interval, under external fare collection and all door boarding:\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 157-158: code points 1-38

$ x_\text{dock} = {T_0 * N_\text{bus} +  [ (P_b * t_b) + (P_a *t_a) ] \over \Delta t} $\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 160-160: code points 1-89

Where:\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 162-163: code points 1-0

\begin{itemize}
\item $ x_\text{dock} $: Saturation level of a docking bay for a given time interval;% @ guide/07-system-speed/understanding-station-saturation.src: lines 163-164: code points 1-0
\item $ N_\text{bus} $: Number of vehicles that use the docking bay during the interval (if the time interval is an hour it is the usual frequency of services referenced in vehicles per hour);% @ guide/07-system-speed/understanding-station-saturation.src: lines 164-166: code points 1-0
\item $ T_0 $: Average dead time per bus;% @ guide/07-system-speed/understanding-station-saturation.src: lines 166-167: code points 1-0
\item $ t_b $: Average boarding time per customer;% @ guide/07-system-speed/understanding-station-saturation.src: lines 167-168: code points 1-0
\item $ t_a $: Average alighting time per customer;% @ guide/07-system-speed/understanding-station-saturation.src: lines 168-169: code points 1-0
\item $ P_b $: Number of boarding customers at docking bay during interval;% @ guide/07-system-speed/understanding-station-saturation.src: lines 169-170: code points 1-0
\item $ P_a $: Number of alighting customers at docking bay during interval;% @ guide/07-system-speed/understanding-station-saturation.src: lines 170-171: code points 1-0
\item $ \Delta t $: Time interval duration (this is usually seen as 3,600 since one hour calculation is normally done and other times are given in seconds).% @ guide/07-system-speed/understanding-station-saturation.src: lines 171-172: code points 1-38
\end{itemize}
% @ guide/07-system-speed/understanding-station-saturation.src: lines 163-172: code points 1-38

The equation shows that for a given demand (number of customers) using a docking bay to board and alight, the way to reduce saturation is by minimizing the time needed to board or alight or by reducing the number of buses, given that the dead time cannot be significantly reduced. The remaining alternative is to split the demand in more docking bays.\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 174-177: code points 1-32

To illustrate the saturation calculation, we take the example of a TransMilenio’s, Modulo A2 of Alcalá Station (March 8, 2007, data) where between 7:00 a.m. and 8:00 a.m. there were 62 vehicles stopping, 975 boarding customers, and 23 alighting customers. The dead time is 15 seconds, boarding time per customer is 0.3 seconds, and alighting time per customer is 0.2 seconds. This way, the saturation level between 7:00 a.m. and 8:00 a.m. can be estimated as:\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 179-183: code points 1-44

$ x = {15 {\text{sec} \over \text{vehicle}} * 62 \text{vehicles} +[( 975 \text{pax} * 0.3{\text{sec} \over \text{pax}}) + (23 \text{pax} * 0.2 {\text{sec} \over \text{pax}})] \over 3,600 \text{sec}} $\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 185-185: code points 1-202

$ x = {930 \text{sec} + [(292.5 \text{sec}) + (4.6 \text{sec})] \over 3,600 \text{sec}} = {409 \text{sec} \over 3,600 {sec}} = 0.11 $\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 187-187: code points 1-135

Understanding saturation is important because queue formation is a function of it.\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 189-189: code points 1-82

According to the theory of queues, the expected queue a vehicle faces when approaching a station would be given by Equation 7.7\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 191-192: code points 1-24

Eq. 7.7 Queue size:\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 194-194: code points 1-19

$ \text{QueueSize}_\text{dock} = { 0.5 * (Irr_\text{arrival}+ Irr_\text{departure})* x_\text{dock} ^ 2 \over (1-x_\text{dock})} $\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 196-196: code points 1-131

Where:\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 198-198: code points 1-6

\begin{itemize}
\item $ \text{QueueSize}_\text{dock} $: Average or expected queueing size at docking bay;% @ guide/07-system-speed/understanding-station-saturation.src: lines 200-201: code points 1-0
\item $ x_\text{dock} $: Saturation level of a docking bay for a given time interval; xdock = Saturation of a docking bay;% @ guide/07-system-speed/understanding-station-saturation.src: lines 201-202: code points 1-0
\item $ Irr_\text{arrival} $: Irregularity of arrivals ($= {\text{Variance}_\text{arrivals' intervals} \over \text{Mean}_\text{arrivals' intervals} ^2 } $);% @ guide/07-system-speed/understanding-station-saturation.src: lines 202-203: code points 1-0
\item $ Irr_\text{departure} $: Irregularity of departures ($= {\text{Variance}_\text{departures' intervals} \over \text{Mean}_\text{departures' intervals} ^2 } $).% @ guide/07-system-speed/understanding-station-saturation.src: lines 203-203: code points 1-168
\end{itemize}
% @ guide/07-system-speed/understanding-station-saturation.src: lines 200-203: code points 1-168

The mean of the intervals for arrivals and departures is the headway, and the variance would be similar to the variance for boardings and alightings if there were no traffic lights and starting schedules were exactly followed. If a service was totally regular, no queues would be expected. The particular case when irregularities for arrival and departure are random ($ Irr_\text{arrivals} = Irr_\text{departures} = 1 $) would result in the queue given by Equation 7.8.\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 205-209: code points 1-32

Eq. 7.8 Queue size for random arrivals and random boarding-alighting times (the mean can be known):\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 211-211: code points 1-99

$ \text{QueueSize}_\text{dock} = { x_\text{dock} ^ 2 \over (1-x_\text{dock})} $\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 213-213: code points 1-81

Where:\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 215-215: code points 1-6

\begin{itemize}
\item $ \text{QueueSize}_\text{dock} $: Average or expected queueing size at docking bay;% @ guide/07-system-speed/understanding-station-saturation.src: lines 217-218: code points 1-0
\item $ x_\text{dock} $: Saturation level of a docking bay for a given time interval.% @ guide/07-system-speed/understanding-station-saturation.src: lines 218-218: code points 1-87
\end{itemize}
% @ guide/07-system-speed/understanding-station-saturation.src: lines 217-218: code points 1-87

For the common irregularities observed in urban busways with high volumes (headways below one minute), Equation 7.9, derived from observation, can be used:\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 220-221: code points 1-52

Eq. 7.9 Queue size for busways in typical urban conditions:\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 223-223: code points 1-59

$ \text{QueueSize}_\text{dock} = { { 0.7 * x_\text{dock}^2} \over (1-x_\text{dock})} $\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 225-225: code points 1-88

Where:\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 227-228: code points 1-0

\begin{itemize}
\item $ \text{QueueSize}_\text{dock}$: Average or expected queueing size at docking bay;% @ guide/07-system-speed/understanding-station-saturation.src: lines 228-229: code points 1-0
\item $ x_\text{dock}$: Saturation level of a docking bay for a given time interval.% @ guide/07-system-speed/understanding-station-saturation.src: lines 229-229: code points 1-86
\end{itemize}
% @ guide/07-system-speed/understanding-station-saturation.src: lines 228-229: code points 1-86

The expected (average) waiting time in queue ($T_\text{queue}$) is given by the queue size multiplied by the average headway, as expressed in Equation 7.10:\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 231-232: code points 1-47

Eq. 7.10 Time waiting in queue:\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 234-234: code points 1-31

$ T_\text{queue} = \text{QueueSize} * \text{Hdwy} $\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 236-236: code points 1-53

Where:\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 238-239: code points 1-0

\begin{itemize}
\item $ T_\text{queue} $: Time waiting in queue;% @ guide/07-system-speed/understanding-station-saturation.src: lines 239-240: code points 1-0
\item $ QueueSize $: Average or expected queueing size;% @ guide/07-system-speed/understanding-station-saturation.src: lines 240-241: code points 1-0
\item $ \text{Hdwy}:$ Average headway.% @ guide/07-system-speed/understanding-station-saturation.src: lines 241-241: code points 1-40
\end{itemize}
% @ guide/07-system-speed/understanding-station-saturation.src: lines 239-241: code points 1-40

\ifxetex
	% disabled for now
\else
	{  % group required to avoid fignote settings escaping
		\img{\hsize}{operations/assets/25ee2e33111ab238ef41d9024257254378fb9f89.jpeg}
		\fignote{5}{The Impact of saturation on vehicle velocity is bigger the closer the stations are to each other as each station implies more queueing delay.\label{chapter:system-speed-and-capacity:figure:the-impact-of-saturation-on-vehicle-velocity-is-bigger-the-closer-the-stations-are-to-each-other-as-each-station-implies-more-queueing-delay}}{Elebeta.}
	}
\fi

A low saturation level or a high level of service means that there are no vehicles queuing at a docking bay. A high saturation level means that there will be long queues at docking bays. Therefore, increasing station saturation tends to lead to a gradual deterioration of service quality. For saturation levels over one ($ x > 1 $), the system is unstable with queues increasing until the demand is reduced after the peak period.\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 246-250: code points 1-12

For the previous example, the module of Alcalá station has a low saturation rate (0.11). At this level, one should not expect to have congestion problems at the station, as the expected queue size and time would be irrelevant:\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 252-254: code points 1-20

$ \text{QueueSize}_\text{dock} = { { 0.7 * x_\text{dock}^2} \over (1-x_\text{dock})}
= { 0.7 * 0.11^2 \over (1-0.11) } = 0.0095 $\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 256-257: code points 1-45

$  T_\text{queue} = \text{QueueSize} * \text{Hdwy} = 0.0095 \text{vehicles} * {1 \over 62 {\text{vehicles} \over \text{hour}}} = 0.00153 \text{hour}= 0.55 \text{sec} $\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 259-259: code points 1-169

The decision about which saturation levels can be tolerated in specific locations should be considered within the overall design context. However, one must keep in mind that for saturation levels above 0.60 (or 60 percent), the risk of severe congestion and system breakdown is considerable. For BRT stations, 40 percent is the maximum accepted for planning purposes, allowing a reasonable safety margin for uncertainties in the planning process. That is, the cumulative time that a vehicle is stopped at a station should not exceed 40 percent of the total time available in an hour.\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 261-266: code points 1-57

Expected station saturation effects on commercial speeds are shown in Figure 7.5. Bogotá’s TransMilenio strives to keep the saturation value in the range of 0.35 to 0.45. The Calle 76 station of the TransMilenio system illustrates the importance of accurately projecting customer movements and station saturation levels. Originally, this critical station was planned for a saturation level of 0.40, but during actual operation the saturation reached 0.65 due to an unexpectedly high number of transfers. Similar levels of saturation were experienced at Heroes Station (which has subsequently been expanded), where saturation was much higher than the 0.40 level anticipated. This experience shows the importance of being somewhat conservative by planning for 40 percent station saturation.\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 268-275: code points 1-59

When boarding is made through different doors, one could expect saturation to be calculated as given by Equation 7.11.\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 277-278: code points 1-17

Eq. 7.11 Simplified saturation level of a docking bay where boarding is through specific doors:\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 280-280: code points 1-95

$ x_\text{dock} = {T_0 * N_\text{bus} +  Max(p_b *t_b,p_a * t_a)  \over \Delta t} $\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 282-282: code points 1-85

Equation 7.11 works when one of the movements between boarding and alighting is clearly longer or if customers arrive at stations in a completely regular fashion between the bus passages (the longer the headway the more regular this number is). But if the times for boarding and alighting are similar, then there will be a certain number of times when boarding will end first and a certain number of times when alighting will end first. If the expected times for each operation were equal, then each operation would be expected to end first half of the time.\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 297-302: code points 1-42

For a random distribution of boarding and alighting times (where the average times are known), the number of occasions that the one operation will be longer than the other is given by Equation 7.12.\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 304-305: code points 1-99

Eq. 7.12 Expected occurrence of the shortest time overcoming the longer:\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 307-307: code points 1-72

$ rf1 = {\overline{t1} \over \overline{t1} + \overline{t2}}$\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 309-309: code points 1-62

Where:\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 311-312: code points 1-0

\begin{itemize}
\item $ rf1 $: Relative frequency where event 1 is longer than event 2;% @ guide/07-system-speed/understanding-station-saturation.src: lines 312-313: code points 1-0
\item $ \overline{t1} $: Average length of distribution of events 1;% @ guide/07-system-speed/understanding-station-saturation.src: lines 313-314: code points 1-0
\item $ \overline{t2} $: Average length of distribution of events 2;% @ guide/07-system-speed/understanding-station-saturation.src: lines 314-314: code points 1-70
\end{itemize}
% @ guide/07-system-speed/understanding-station-saturation.src: lines 312-314: code points 1-70

Using the Poisson distribution, a discrete probability distribution, for events 1 and 2, where the standard deviation is equal to the mean, is assumed.\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 316-317: code points 1-52

If we call the mean boarding time per bus of $ T_b $, with $ T_b = t_b * P_b $, and mean alighting time per bus of $ Ta $, with $T_a = t_a * p_a $, the average dwell time would be given by Equation 7.13.\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 319-320: code points 1-95

Eq. 7.13 Saturation level of a docking bay where boarding is through specific doors:\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 322-322: code points 1-84

$ T_d = T_0 + T_a + ({T_b \over T_a + T_b})  * T_b = T_0 + T_b + ({T_a \over T_a + T_b}) * T_a $\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 324-324: code points 1-98

Where:\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 326-327: code points 1-0

\begin{itemize}
\item $ T_d $: Average dwell time for all buses on a docking bay;% @ guide/07-system-speed/understanding-station-saturation.src: lines 327-328: code points 1-0
\item $ T_o $: Average dead time for all buses on a docking bay;% @ guide/07-system-speed/understanding-station-saturation.src: lines 328-329: code points 1-0
\item $ T_a $: Average alighting time for all buses on a docking bay;% @ guide/07-system-speed/understanding-station-saturation.src: lines 329-330: code points 1-0
\item $ Tb  $: Average boarding time for all buses on a docking bay;% @ guide/07-system-speed/understanding-station-saturation.src: lines 330-331: code points 1-0
\item $ T_b \over T_a + T_b $: Relative frequency that boarding will be longer than alighting;% @ guide/07-system-speed/understanding-station-saturation.src: lines 331-332: code points 1-0
\item $ T_a \over T_a + T_b $: Relative number of times that alighting will be longer than boarding.% @ guide/07-system-speed/understanding-station-saturation.src: lines 332-332: code points 1-102
\end{itemize}
% @ guide/07-system-speed/understanding-station-saturation.src: lines 327-332: code points 1-102

Therefore the saturation for a docking bay where boarding is made through specific doors is given by Equation 7.14.\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 334-335: code points 1-14

Eq. 7.14 Saturation calculation when boarding is through a specific door:\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 337-337: code points 1-73

$ x_\text{dock} = {T_0 * N_\text{bus} + P_b * t_b + ({P_a * t_a \over P_a * t_a + P_b * t_b}) * P_a * t_a \over \Delta t} $\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 339-339: code points 1-125

Where:\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 341-342: code points 1-0

\begin{itemize}
\item $ x_\text{dock} $: Saturation level of a docking bay for a given time interval;% @ guide/07-system-speed/understanding-station-saturation.src: lines 342-343: code points 1-0
\item $ N_\text{bus}: $: Number of vehicles that use the docking bay during the interval (if the time interval is an hour it is the usual frequency of services referenced in vehicles per hour);% @ guide/07-system-speed/understanding-station-saturation.src: lines 343-345: code points 1-0
\item $ T_0 $: Average dead time per bus;% @ guide/07-system-speed/understanding-station-saturation.src: lines 345-346: code points 1-0
\item $ t_b $: Average boarding time per customer;% @ guide/07-system-speed/understanding-station-saturation.src: lines 346-347: code points 1-0
\item $ t_a $: Average alighting time per customer;% @ guide/07-system-speed/understanding-station-saturation.src: lines 347-348: code points 1-0
\item $ P_b $: Number of boarding customers at docking bay during interval (average boarding time per bus is given by $ T_b = t_b * P_b $);% @ guide/07-system-speed/understanding-station-saturation.src: lines 348-349: code points 1-0
\item $ P_a $: Number of alighting customers at docking bay during interval(average alighting time per bus is given by $ T_a = t_a * P_a $;% @ guide/07-system-speed/understanding-station-saturation.src: lines 349-350: code points 1-0
\item $ \Delta t $: Time interval duration (this is usually seen as 3,600 since one hour calculation is normally done and other times are given in seconds).% @ guide/07-system-speed/understanding-station-saturation.src: lines 350-351: code points 1-38
\end{itemize}
% @ guide/07-system-speed/understanding-station-saturation.src: lines 342-351: code points 1-38

For example, a regular bus stop in front of a children’s hospital in São Paulo, using data from between 6:00 a.m. and 7:00 a.m. on February 2, 2007, is served by two routes with a frequency of four trips per hour each. The average number of boardings per vehicle is 4.1, for a total of 33, and the average number of alightings per vehicle is 10, for a total of 80. People carrying babies count as one. The average time per person for boarding is five seconds, and for alighting it is three seconds, with one wheelchair alighting taking around forty-five seconds. The saturation of this stop would be calculated as:\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 353-358: code points 1-95

$ x_\text{dock} = {T_0 * N_\text{bus} + P_b * t_b + ({P_a * t_a \over P_a * t_a + P_b * t_b}) * P_a * t_a \over \Delta t} $\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 360-360: code points 1-125

$ x = {16 {\text{sec} \over \text{vehicle}} * 8 \text{vehicles} +( 33 \text{pax} * 5 {\text{sec} \over \text{pax}}) +  ({80*3.0  \over 80*3.0 +33*5}) * 80 \text{pax} * 3.0 {\text{sec} \over \text{pax}} \over 3,600 \text{sec}} $\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 363-363: code points 1-229

$ x = {128 \text{sec} + (165 \text{sec}) + ({240 \over 240 + 165}) * 240 \text{sec}] \over 3,600 \text{sec}} $\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 365-365: code points 1-112

$ x = {128 \text{sec} + (165 \text{sec}) + ({240 \over 405}) * 240 \text{sec}] \over 3,600 \text{sec}} $\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 367-367: code points 1-106

$ x = {128 \text{sec} + (165 \text{sec}) +  0.59 * 240 \text{sec} \over 3,600 \text{sec}} = 0.12 $\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 369-369: code points 1-100

With the stop being occupied only 12 percent of the time, the average expected queue is 0.012 (each hundred arrivals the stop will be used by another bus), and the average queue delay is 5 seconds, as calculated below:\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 371-373: code points 1-17

$ \text{QueueSize} = { 0.7 * x_\text{dock}^2 \over (1-x_\text{dock})}
= { 0.7 * 0.12^2 \over (1-0.12) } = 0.0116 $\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 375-376: code points 1-45

$  T_\text{queue} = \text{QueueSize} * \text{Hdwy} = 0.0116 \text{vehicles} * {1 \over 8 {\text{vehicles} \over \text{hour}}} = 0.001455
\text{hour}= 5.23 \text{sec} $\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 378-379: code points 1-31

\subsection{1}{Saturation on a Sub-Stop with Many Docking Bays}
\label{volume:operations:chapter:system-speed-and-capacity:section:understanding-station-saturation:subSection:saturation-on-a-sub-stop-with-many-docking-bays}
% @ guide/07-system-speed/understanding-station-saturation.src: lines 381-510: code points 1-0

The defining characteristic of a sub-stop with two or more docking bays is that a vehicle can leave the sub-stop only if the vehicle in front of it has already left. We can assume that if this design is under consideration, the saturation for only one docking bay is relatively high, so we may ignore the situations where frequency is low and dwell times are short. We are facing something between two extreme situations:\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 383-388: code points 1-0

\begin{itemize}
\item Low frequencies with many customers boarding and alighting in a terminal-like situation;% @ guide/07-system-speed/understanding-station-saturation.src: lines 388-389: code points 1-0
\item High frequencies with short dwell time.% @ guide/07-system-speed/understanding-station-saturation.src: lines 389-389: code points 1-45
\end{itemize}
% @ guide/07-system-speed/understanding-station-saturation.src: lines 388-389: code points 1-45

Of course, another situation could be when there is a high boarding-alighting demand with high service frequencies without the use of overtaking lanes.\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 391-392: code points 1-48

\emphasis{Same service at all docks}\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 394-394: code points 1-27

If the vehicles that dwell at the sub-stop with N docking bays were all serving the same route and the vehicles were riding in a platoon (of N vehicles), this would be equivalent to having a longer vehicle (like a train): the number of doors would be multiplied by N, both the boarding time and the frequency of this longer vehicle would be the original frequency divided by N too, which in this case, saturation would be divided by N.\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 396-400: code points 1-22

That is ignoring that the dead time for the “train” would be slightly longer than that of one vehicle alone; this difference would not be meaningful considering the lack of precision in the previously proposed Equation 7.3 when there is no queue.\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 402-404: code points 1-36

But when the bay is congested (i.e., is a queue moving), which is the likely situation when multiple docking bays are under consideration, the dead time for the whole convoy would be given by Equation 7.15.\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 406-407: code points 1-105

Eq. 7.15 Dead time for convoy:\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 409-409: code points 1-30

$ T_{0\text{convoy}} = 13 + 0.25 * L_\text{vehicle} * 0.25 + (2 + 0.17 * L_\text{vehicle}) * (N - 1) $\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 411-411: code points 1-104

Where:\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 413-413: code points 1-6

\begin{itemize}
\item $ T_{0 convoy}$: Dead time in seconds for the whole convoy;% @ guide/07-system-speed/understanding-station-saturation.src: lines 415-416: code points 1-0
\item $ N $: Number of vehicles in the convoy;% @ guide/07-system-speed/understanding-station-saturation.src: lines 416-417: code points 1-0
\item $ L_\text{Vehicle} $: Length of each convoy vehicle in meters (average vehicle length when vehicles are different).% @ guide/07-system-speed/understanding-station-saturation.src: lines 417-417: code points 1-123
\end{itemize}
% @ guide/07-system-speed/understanding-station-saturation.src: lines 415-417: code points 1-123

\emphasis{Ordered platoon (routes attributed to docks)}\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 419-419: code points 1-46

If the routes stopping at the sub-stop are different, but platoons are organized when entering the busway (Figure 7.6) always in an order that customers can be positioned on the platform in front of the right docking bay, the whole “train” would leave the station once boarding and alighting in the most heavily demanded “car” was completed.\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 421-424: code points 1-37

If the number of arrivals of customers at the station was regular and equally distributed at all routes, the saturation for the sub-stop would be equal to the saturation of the most congested docking bay, when an average queue size is N times bigger.\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 426-428: code points 1-40

But if the number of customers that arrive at a station to board each route is variable, then an additional dwell time must be added to calculate the saturation of the most congested docking bay. To do this, within that variability, there is an expected regularity, with the known mean and equal standard deviation value of the worst case. This is a situation similar to boarding and alighting through different doors, as at certain times the boarding-alighting times of each platform may overcome the others.\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 430-434: code points 1-87

Assuming $T_{i b/a}$ is the average boarding and alighting time calculated for the ith docking bay, if there were two docking bays, the average dwell time for the convoy would be given by Equation 7.16.\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 436-437: code points 1-93

Eq. 7.16 Average dwell time for two docking bays in a sub-stop:\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 439-439: code points 1-63

$ T_\text{d 2 bays} = T_0 + T_1 + T_2 -  {1 \over {1 \over T_1} + {1 \over T2}} =  T_0 + T_1 + {T_2 \over T1+T2 } *  T2 $\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 441-441: code points 1-123

If there are three platforms, the equation (shown in 7.17) gets a bit longer.\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 443-443: code points 1-77

Eq. 7.17 Average dwell time for three docking bays in a sub-stop:\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 445-445: code points 1-65

$ T_\text{d 3 bays} = T_0 + T_1 + T_2 +T_3 $\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 447-447: code points 1-46

$ - {1 \over {1 \over T_1} + {1 \over T2}} 
- {1 \over {1 \over T_1 } + {1 \over T3}}  
- {1 \over {1 \over T_2 } + {1 \over T3}} $\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 449-451: code points 1-44

$ + {1 \over {1 \over T_1 } + {1 \over T_2 } + {1 \over T3}} $\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 453-453: code points 1-64

For four platforms it gets even longer and becomes too impractical and difficult to apply.\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 455-455: code points 1-90

Eq. 7.18 Average dwell time for four docking bays in a sub-stop:\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 457-457: code points 1-64

$ T_\text{d 4 bays} = T_0 + T_1 + T_2 + T_3 + T_4 $\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 459-459: code points 1-53

$ - {1 \over {1 \over T_1} + {1 \over T2}} 
- {1 \over {1 \over T_1} + {1 \over T3}}  
- {1 \over {1 \over T_1} + {1 \over T4}} 
- {1 \over {1 \over T_2} + {1 \over T3}} 
- {1 \over {1 \over T_2} + {1 \over T4}} 
- {1 \over {1 \over T_3} + {1 \over T4}} $\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 461-466: code points 1-43

$ + {1 \over {1 \over T_1} + {1 \over T_2} + {1 \over T3}} 
+ {1 \over {1 \over T_1} + {1 \over T_3} + {1 \over T4}} 
+ {1 \over {1 \over T_2} + {1 \over T_3} + {1 \over T4}} $\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 468-470: code points 1-59

$ - {1 \over {1 \over T_1} + {1 \over T_2} + {1 \over T_3} + {1 \over T4}} $\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 472-472: code points 1-78

Where:\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 474-474: code points 1-6

\begin{itemize}
\item $ T_\text{d N bays} $: Average dwell time for a sub-stop with N docking bays (equation will have $2^N $ terms);% @ guide/07-system-speed/understanding-station-saturation.src: lines 476-477: code points 1-0
\item $ T_0 $: Dead time (given by Equation 7.15);% @ guide/07-system-speed/understanding-station-saturation.src: lines 477-478: code points 1-0
\item $ T_i $: Variable dwell time for ith docking bay calculated in isolation (given by Equation 7.4 or 7.13 minus $ T_0$).% @ guide/07-system-speed/understanding-station-saturation.src: lines 478-479: code points 1-16
\end{itemize}
% @ guide/07-system-speed/understanding-station-saturation.src: lines 476-479: code points 1-16

This equation can be extended to any number of platforms and can be relatively easily implemented by an algorithm, but an alternative formula (Equation 7.19) that underestimates saturation by 2 percent for each additional docking bay above two can be used for practical purposes.\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 481-483: code points 1-77

Eq. 7.19: Practical approximation for average dwell time for N docking bays in a sub-stop:\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 485-485: code points 1-90

$T_\text{d N bays}: T_0 + {3 \over N + 2}  * \sum_{i=1} ^N {T_i} $\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 487-487: code points 1-68

Where:\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 489-490: code points 1-0

\begin{itemize}
\item $ T_\text{d N bays}$: Average dwell time for a sub-stop with N bays (equation will have 2N terms);% @ guide/07-system-speed/understanding-station-saturation.src: lines 490-491: code points 1-0
\item $ N $: Number of vehicles in the convoy;% @ guide/07-system-speed/understanding-station-saturation.src: lines 491-492: code points 1-0
\item $ T_0 $: Dead time (given by Equation 7.15);% @ guide/07-system-speed/understanding-station-saturation.src: lines 492-493: code points 1-0
\item $ T_i $: Variable dwell time for ith docking bay calculated in isolation (given by Equation 7.4 or 7.13 without considering $T_\text{0}$ there).% @ guide/07-system-speed/understanding-station-saturation.src: lines 493-494: code points 1-42
\end{itemize}
% @ guide/07-system-speed/understanding-station-saturation.src: lines 490-494: code points 1-42

\ifxetex
	% disabled for now
\else
	{  % group required to avoid fignote settings escaping
		\img{\hsize}{operations/assets/6998bc0a9a153e253e8fa914ac0edc00e9cb26d2.png}
		\fignote{6}{Convoying systems order vehicles when entering the corridor, so they ride as “train” and the docking position for every route at the station is fixed, preventing users from running up and down the platform.\label{chapter:system-speed-and-capacity:figure:convoying-systems-order-vehicles-when-entering-the-corridor-so-they-ride-as-train-and-the-docking-position-for-every-route-at-the-station-is-fixed-preventing-users-from-running-up-and-down-the-platform}}{Pedro Szász.}
	}
\fi

\emphasis{Unordered vehicles}\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 500-500: code points 1-21

If the routes stopping at the sub-stops are different and may arrive in any order, the approximation for saturation is the same, but what is observed in reality is that for sub-stops with three or more docking bays, a certain quantity of vehicles stops twice to pick up customers that miss the vehicle dwelling at the first time. Unless there is operational enforcement for preventing this second stop, this can be seen as enforcing bad service.\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 502-506: code points 1-33

In a design situation, more than three docking bays without organized convoys should not be proposed. If dealing with an existing situation, the safest thing to do is to measure the existing saturation.\par
% @ guide/07-system-speed/understanding-station-saturation.src: lines 508-510: code points 1-0

\section{4}{Calculating Corridor Capacity}
\label{volume:operations:chapter:system-speed-and-capacity:section:calculating-corridor-capacity}
% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 1-324: code points 1-30

\quotation{If I have the belief that I can do it, I shall surely acquire the capacity to do it even if I may not have it at the beginning.}{Mahatma Gandhi, political and spiritual leader, 1847–1948}
% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 3-4: code points 1-59

\subsection{1}{Corridor Capacity at Station Calculation}
\label{volume:operations:chapter:system-speed-and-capacity:section:calculating-corridor-capacity:subSection:corridor-capacity-at-station-calculation}
% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 6-144: code points 1-14

The provision of high capacities without creating undue saturation is a principal design consideration of most BRT systems. Once the bottleneck of the system is the station, the capacity of a BRT corridor is given by the capacity of its busiest station section, which is basically dependent on the vehicle size, load factor, and sum of service frequencies dwelling at each sub-stop with those who “jump” the station (direct services). Equation 7.20 below shows this.\par
% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 8-12: code points 1-58

Eq. 7.20 Corridor capacity at the station:\par
% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 14-14: code points 1-42

$ \text{CorridorCapacity}_\text{at station} = \text{VSize} *\text{LoadFactor}*(\text{Freq}_\text{direct} + \sum_{i=1}^{N_\text{sub-stops} \text{Freq}_i}) $\par
% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 16-16: code points 1-157

Where:\par
% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 18-18: code points 1-6

\begin{itemize}
\item $ \text{Corridor Capacity}_\text{at station} $: Number of people the corridor can transport through a given station section, in passengers per hour per direction (pphpd);% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 20-22: code points 1-0
\item $ \text{VSize} $: Maximum vehicle capacity;% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 22-23: code points 1-0
\item $ \text{LoadFactor} $: Average occupancy of the vehicles at the station, expressed as a proportion of the maximum vehicle capacity;% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 23-25: code points 1-0
\item $ \text{Freq}_\text{direct} $: Frequency of limited-stop services that skip the station under analysis;% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 25-26: code points 1-0
\item $ \text{Freq}_i $ Vehicle frequency at sub-stop i is the number of vehicles that dwells at all docking bays of sub-stop i during the evaluated interval (usually an hour);% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 26-28: code points 1-0
\item $ N_\text{sub-stops} $: Number of independent sub-stops in each station.% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 28-28: code points 1-80
\end{itemize}
% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 20-28: code points 1-80

For a more generic evaluation, assuming the frequency in each sub-stop is the same, the corridor capacity at the station is:\par
% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 30-31: code points 1-18

Eq. 7.21 Basic formula for corridor capacity at station\par
% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 33-33: code points 1-55

$ \text{CorridorCapacity}_\text{at station} = \text{VSize} * \text{LoadFactor} * (\text{Freq}_\text{direct} + \text{NSubstops} *\text{Freq}_\text{sub-stop}) $\par
% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 35-35: code points 1-160

Table 7.2 shows sample corridor capacities for a range of common scenarios without limited-stop services. By varying only the vehicle capacity and the number of sub-stops per station, it shows just how powerful these two factors are in determining system capacity. It must be noted that the actual potential capacities for a given city will not be influenced by other system characteristics. But it should also be noticed that the table and the equation do not reveal anything about system speed.\par
% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 37-41: code points 1-74

\tabletitle{2}{BRT Corridor Capacity at Station Scenarios}
\label{chapter:system-speed-and-capacity:table:brt-corridor-capacity-at-station-scenarios}
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% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 43-116: code points 1-9

Source: Institute of Transportation Studies, University of California, Berkeley\par
% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 118-118: code points 1-79

The value of sixty vehicles per hour at a sub-stop with one docking bay is a common and relatively easy way to achieve value—that is, one vehicle per minute. A regular bus stop (let us say a curbside stop in a congested mixed traffic environment) with space for two 12-meter vehicles loading simultaneously would handle 60 vehicles per hour, while the average number of boardings per bus is not bigger than 30 customers (which is one-third of such bus maximum capacity); we assume 20 seconds of dead time (dead time is shorter under congested conditions) and 3 seconds per boarding customer (which is achievable with one step boarding at one door as long as fare collection is in the middle the bus).\par
% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 120-126: code points 1-88

The tricky part of the design is achieving that frequency without forming queues at the station. For the example of the last paragraph, at a one docking bay capacity, one should expect a queue of 12 vehicles after one hour of operation, 24 vehicles after two hours of operation, and so on. If instead of an average of 30 customers boarding per bus, the average would be 20 customers boarding per bus, a saturation level of 75 percent, after one hour of operation the expected queue would be of 2.5 buses and of similar size after that. This still would mean an extra delay of two minutes for all customers inside the bus at this bus stop. The total time at the station would be three minutes instead of two. That is quite a lot if compared with TransMilenio (see the example illustrating Equation 7.1) where the time at a station for twenty customers to board would be twenty-one seconds.\par
% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 128-136: code points 1-54

The number of docking bays can also create serious problems, unless operating in a controlled convoy, a station split in two curbside stops as in the previous example (operating at a 75 percent saturation level) would have to be apart from each other by 100 meters to create queueing places for the buses (even the average queue is of 2 to 3 vehicles, it will likely reach 7 vehicles queueing behind the 2 already loading in the course of an hour of operation), otherwise the vehicles forming the queue for boarding at the front pole will block the exit of buses loading at the back pole, creating more queues, resulting in congestion.\par
% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 138-144: code points 1-14

\subsection{2}{Detailed Capacity Calculation}
\label{volume:operations:chapter:system-speed-and-capacity:section:calculating-corridor-capacity:subSection:detailed-capacity-calculation}
% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 146-324: code points 1-30

While Chapter 6: Service Planning provided guidance on how to select routes to include on the corridor (Section 6.5.3) and to skip stations (Section 6.6.4) so that operational capacity was not surpassed, the following sections of this chapter (Sections 7.5 and 7.6) evaluate how system characteristics will affect operational capacity, so proper features can be selected to reach the required operational capacity.\par
% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 148-151: code points 1-107

The capacity calculation for a station given earlier (Equation 7.20), applied to the segment with the higher number of services, would give the system capacity.\par
% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 153-154: code points 1-58

If a design is proposed where saturations are above 40 percent, it can be considered above operational capacity and adjustments have to be made. As an example, let us consider a corridor where the demand study indicated that the boarding and alighting requirements per station in one direction at the morning peak hour are as given in Table 7.3, for the first and tenth years of operation.\par
% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 156-159: code points 1-80

\tabletitle{3}{Corridor Demand Example}
\label{chapter:system-speed-and-capacity:table:corridor-demand-example}
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% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 161-184: code points 1-9

The load of all segments is already defined, as the load of the preceding segment plus boarding of preceding station minus alighting at the preceding station.\par
% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 186-187: code points 1-84

\ifxetex
	% disabled for now
\else
	{  % group required to avoid fignote settings escaping
		\img{\hsize}{operations/assets/7bcfc79f56861f68f444b5b0156e17eca013400a.jpeg}
		\fignote{7}{Example corridor morning peak loads for the tenth year, boarding and alighting per station.\label{chapter:system-speed-and-capacity:figure:example-corridor-morning-peak-loads-for-the-tenth-year-boarding-and-alighting-per-station}}{Elebeta.}
	}
\fi

For the beginning year of operations (year 0), an initial service plan proposed a closed trunk corridor with twenty stations, with one service stopping at all stations. The service will be operated using a seventy-passenger vehicle with a headway of one minute.\par
% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 192-194: code points 1-55

The proposed standard station will have an elevated platform with external fare collection, resulting in boarding or alighting times of one second per customer (for either of the two vehicle doors).\par
% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 196-197: code points 1-96

The supply is defined as 4,200 pphpd (60 vehicles per hour multiplied by 70 passengers per vehicle) for the entire corridor. The system capacity, by using a design load factor of 0.85 in order to avoid overcrowding due to irregularity, is 3,570 pphpd.\par
% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 199-201: code points 1-49

Table 7.4, which can be reproduced with the formulas discussed in the previous sections of this chapter, shows that the proposed solution leads to a load factor of 0.86 at the critical link, as well as saturation levels above the accepted limit.\par
% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 203-205: code points 1-32


			\ifxetex
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				{  % group required to avoid fignote settings escaping
					\tabletitle{4}{Station Saturation at Example Corridor for First Year Initial Proposal}
\label{chapter:system-speed-and-capacity:table:station-saturation-at-example-corridor-for-first-year-initial-proposal}

					\img{\hsize}{operations/assets/0d977510a14fa0cef212e26be9da8ceeaf2796ee.png}
				}
			\fi

To reduce the load factor to the proposed value of 0.85, it is enough to raise service frequency to 61 vehicles per hour. While only one more bus per hour, this is nearly a 2 percent capacity increase.\par
% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 211-213: code points 1-26

If instead of using a 2-door vehicle, a 3-door vehicle is proposed, with a reduction of boarding or alighting time from 1.0 second per customer to 0.7 seconds per customer, then saturation gets closer to an acceptable level, as shown in Table 7.5.\par
% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 215-217: code points 1-46


			\ifxetex
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				{  % group required to avoid fignote settings escaping
					\tabletitle{5}{Station Saturation at Example Corridor for First Year Second Proposal}
\label{chapter:system-speed-and-capacity:table:station-saturation-at-example-corridor-for-first-year-second-proposal}

					\img{\hsize}{operations/assets/cc8302a28088e9d53d994f18d04eff6c19e6566c.png}
				}
			\fi

If such levels were found at the tenth year of operation, one could tolerate this proposal, but as the demand will grow, this saturation is clearly not acceptable.\par
% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 223-224: code points 1-86

Indeed, looking at the MaxLoad for the tenth year of operation, 4,505 passengers per hour, after station G (shown in Table 7.6), one can tell that 76 vehicles per hour will be required (capacity equal to 4,522 pphpd), and with such frequencies, all stations will be at risk of experiencing severe congestion. The resulting times and speeds in Table 7.6 seem acceptable, but due to planning uncertainties, saturations above the 0.4 limit expose the system to breakdown.\par
% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 226-230: code points 1-51


			\ifxetex
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				{  % group required to avoid fignote settings escaping
					\tabletitle{6}{Station Saturation at Example Corridor for Tenth Year First Proposal}
\label{chapter:system-speed-and-capacity:table:station-saturation-at-example-corridor-for-tenth-year-first-proposal}

					\img{\hsize}{operations/assets/d222248f28eb6cc56c755e2572a513c740b7f820.png}
				}
			\fi

Vehicle sizes were determined for minimizing the sum of waiting and operating costs. By increasing vehicle size to increase operational capacity, waiting times would also increase, especially in the first years of operation. One of the objectives of the system is to reduce travel times door-to-door, so this has to be weighed in that light. Increasing the vehicle size to a 15-meter vehicle for 100 passengers, however, would allow some stations in the example to perform above operational capacity (4,505 passengers per hour with 53 vehicles per hour) in the tenth year of operation, as shown in Table 7.7, with a more acceptable saturation level.\par
% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 236-242: code points 1-45


			\ifxetex
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				{  % group required to avoid fignote settings escaping
					\tabletitle{7}{Station Saturation at Example Corridor for Tenth Year Increasing Vehicle Size to 100 Passengers}
\label{chapter:system-speed-and-capacity:table:station-saturation-at-example-corridor-for-tenth-year-increasing-vehicle-size-to-100-passengers}

					\img{\hsize}{operations/assets/3cadef881ae3146bdf13f13c356e35d67988bbe1.png}
				}
			\fi

Using an articulated 160-passenger vehicle would bring saturation at the critical station to a comfortable saturation level of 0.34. If the design included a fourth door, reducing boarding time to 0.5 seconds per customer, than saturation at the critical station would be 0.29. But in the first year, frequency would be twenty-six vehicles per hour (over a two-minute headway) and average wait time would be over one minute in the peak, and during the off-peak time this value would likely double or triple.\par
% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 248-253: code points 1-17

Without having to add overtaking lanes at the stations (although overtaking lanes may be feasible without affecting mixed traffic capacity if stations are properly located; see Chapter 24: Intersections and Signal Control), the only solution left keeping those vehicle sizes would be to use two docking bays per station, as shown in Table 7.8. As this is not a congested situation, dead times will not be heavily affected. What is expected is that a certain number of times (proportional to the queue size of Table 7.6), boarding will occur through the second platform of the station. Total passenger queueing time is significantly reduced from 136 hours to 83.\par
% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 255-261: code points 1-43


			\ifxetex
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				{  % group required to avoid fignote settings escaping
					\tabletitle{8}{Station Saturation at Example Corridor for Tenth Year with Two Docking Bays per Station}
\label{chapter:system-speed-and-capacity:table:station-saturation-at-example-corridor-for-tenth-year-with-two-docking-bays-per-station}

					\img{\hsize}{operations/assets/1847c379764266cc71acfa645cec9b4c08cac9a2.png}
				}
			\fi

To consider the provision of overtaking lanes, one must look at the internal origin-destination matrix of the BRT system (in Table 7.9) to identify potential route splits to create limited services. Ideally, these limited routes should have a headway between 2.5 to 3 minutes in order to reduce the effects of irregularity on each route. But in our example, we are initially creating only one limited route that jumps from station E to station L. These stations were selected to avoid 45 seconds of queueing for a large volume of passengers per hour (1,666). The results were quite similar if the limited route jumped stations F to M. The resulting saturation is shown in Table 7.10 assuming that all passengers highlighted in yellow in the matrix will use the limited service, passengers highlighted in pink will be shared by two services proportionally to the frequency, and the remaining passengers (in orange) will use the local service.\par
% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 267-276: code points 1-35


			\ifxetex
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					\tabletitle{9}{Example Corridor Internal O\slash{}D Matrix for the Tenth Year}
\label{chapter:system-speed-and-capacity:table:example-corridor-internal-od-matrix-for-the-tenth-year}

					\img{\hsize}{operations/assets/a72ae7a6f766979bbc5d1e2bc40190ea827863a1.png}
				}
			\fi


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					\tabletitle{10}{Station Saturation at Example Corridor for Tenth Year with One Limited-Stop Service}
\label{chapter:system-speed-and-capacity:table:station-saturation-at-example-corridor-for-tenth-year-with-one-limited-stop-service}

					\img{\hsize}{operations/assets/1a43fca057698080889534f9b14f02697c089ada.png}
				}
			\fi

The additional queue waiting time in relation to the previous alternative is forty seconds, but after the addition of the limited service, the travel time difference between local and limited stop is only twenty-six seconds, which would end up making no difference for users who can take the limited service to use that route. If customers could be forced to behave the way we assumed, the total queueing time would be further reduced from eighty-three to fifty-five. To force this behavior, one can propose, in addition to the limited service, to split the local service into two early return services: the first (Local 1) would attend from station A to station L, and the second (Local 2) from station E to station T.\par
% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 287-294: code points 1-23

Passengers shown in the OD matrix would have the options below:\par
% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 296-297: code points 1-0

\begin{itemize}
\item Light pink: Local 1 or Limited-stop% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 297-298: code points 1-0
\item Light orange: Local 1% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 298-299: code points 1-0
\item Yellow: Limited-stop% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 299-300: code points 1-0
\item Brownish orange: Local 1 or Local 2% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 300-301: code points 1-0
\item Medium orange: Local 2% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 301-302: code points 1-0
\item Darker pink: Limited-stop or Local 2% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 302-302: code points 1-42
\end{itemize}
% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 297-302: code points 1-42

When splitting the original local in two, splitting the original frequency (48 vehicles per hour) is not enough to serve the loads between stations D and E and stations L and M, so the sum of frequencies of this new service (28.5 and 23.1) ends up being higher than the original alternative. Total travel time before the split was 25.6 vehicle-hours (during the peak hour), and after it became 19.2 vehicle-hours, indicating a potential fleet reduction. If a reversion time of five minutes on each side is considered, a symmetrical return and a constant demand (PHtoCC = 0), instead of needing sixty vehicles, only forty-eight are needed to run the local service.\par
% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 304-310: code points 1-36

Besides that, as shown in Table 7.11, there is some meaningful reduction in saturation for stations from A to D and M to T while the stations E to L face a small increase. Passenger time in queue is further reduced to fifty hours, against eighty-three without overtaking lanes and direct service. Figure 7.8 shows the resulting loads in each service.\par
% @ guide/07-system-speed/calculating-corridor-capacity.src: lines 312-315: code points 1-42


			\ifxetex
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					\tabletitle{11}{Station Saturation at Example Corridor for Tenth Year with Limited and Early Return Local Services}
\label{chapter:system-speed-and-capacity:table:station-saturation-at-example-corridor-for-tenth-year-with-limited-and-early-return-local-services}

					\img{\hsize}{operations/assets/a267476e7e06ac7594029350d0705b9ef5effcfe.png}
				}
			\fi

\ifxetex
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		\img{\hsize}{operations/assets/c8f5dc7ead2fa4733313db0fcc48c8c13a55f88b.jpeg}
		\fignote{8}{Example corridor morning peak loads for the tenth year, boarding and alighting per station. Image\label{chapter:system-speed-and-capacity:figure:example-corridor-morning-peak-loads-for-the-tenth-year-boarding-and-alighting-per-station-image}}{Elebeta.}
	}
\fi

\section{5}{Expanding Corridor Capacity}
\label{volume:operations:chapter:system-speed-and-capacity:section:expanding-corridor-capacity}
% @ guide/07-system-speed/expanding-corridor-capacity.src: lines 3-241: code points 1-0

\quotation{Good design begins with honesty, asks tough questions, comes from collaboration and from trusting your intuition.}{Freeman Thomas, designer, 1957–}
% @ guide/07-system-speed/expanding-corridor-capacity.src: lines 5-6: code points 1-33

Various design modifications in BRT services and infrastructure expand the capacity of a BRT system. The goal of these modifications is to carry more customers—while minimizing the saturation level at stations—and thereby ensuring good service speeds.\par
% @ guide/07-system-speed/expanding-corridor-capacity.src: lines 8-10: code points 1-37

\subsection{1}{Increasing the Service Frequency}
\label{volume:operations:chapter:system-speed-and-capacity:section:expanding-corridor-capacity:subSection:increasing-the-service-frequency}
% @ guide/07-system-speed/expanding-corridor-capacity.src: lines 12-25: code points 1-127

The service frequency refers to the number of vehicles per hour. The waiting time between vehicles, which is roughly the same idea, is known as the headway. Up to a point, simply increasing the vehicle frequency on a BRT corridor can increase the corridor capacity. However, at frequencies of more than sixty vehicles per hour, simple BRT corridors with one lane per direction tend to experience an unacceptably high level of vehicle bunching, or saturation. Therefore, the designer must turn to other alternatives, as discussed in the sections below.\par
% @ guide/07-system-speed/expanding-corridor-capacity.src: lines 14-19: code points 1-35

The desired load factor may vary between peak and nonpeak periods. In Bogotá’s TransMilenio system, typical load factors are 80 percent for peak periods and 70 percent for nonpeak periods. But as ridership levels grow in Bogotá, overcrowding becomes an increasing concern (Figure 7.9).\par
% @ guide/07-system-speed/expanding-corridor-capacity.src: lines 21-23: code points 1-79

\ifxetex
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		\img{\hsize}{operations/assets/fd36c768c52baa3480ec002747e39770afe150de.jpeg}
		\fignote{9}{The Avendida Caracas corridor in Bogotá suffers from overcrowding at peak periods.\label{chapter:system-speed-and-capacity:figure:the-avendida-caracas-corridor-in-bogot-suffers-from-overcrowding-at-peak-periods}}{Carlos Pardo.}
	}
\fi

\subsection{2}{Using Larger Vehicles}
\label{volume:operations:chapter:system-speed-and-capacity:section:expanding-corridor-capacity:subSection:using-larger-vehicles}
% @ guide/07-system-speed/expanding-corridor-capacity.src: lines 27-110: code points 1-30

If a corridor is liable to reach saturation by way of the number of vehicles per hour, one solution is to increase the size of the vehicles. Replacing regular 12-meter vehicles with articulated 18-meter vehicles results in a dramatic increase in capacity—without causing a significant worsening in the saturation level. Determining the vehicle size should take into account the costs of vehicle operations against waiting times, as discussed in Section 6.4: Optimizing Vehicle Size and Fleet Size. Stations and service plan alternatives should adapt to use the optimal size to the required demand.\par
% @ guide/07-system-speed/expanding-corridor-capacity.src: lines 29-34: code points 1-78

As has been noted, system designers have many vehicle size options. Table 7.12 summarizes the standard vehicle sizes available to system developers. Higher vehicle capacity can, in the right circumstances, increase BRT system capacity, but the right vehicle size is not always the largest vehicle. The main advantage of larger vehicles stems from reductions in operating costs, especially driver labor costs per customer carried. However, in lower-demand corridors, these large vehicles also tend to mean lower frequency, and hence longer waiting times for customers.\par
% @ guide/07-system-speed/expanding-corridor-capacity.src: lines 36-42: code points 1-0

\tabletitle{12}{Trunk Corridor Vehicle Options}
\label{chapter:system-speed-and-capacity:table:trunk-corridor-vehicle-options}
\halign to 30\tablemodule{%
	\vtop{\sffamily\footnotesize\noindent\hsize=11\tablemodule#}\hbox to 0.5\tablemodule{}&
	\hbox to 0.5\tablemodule{}\vtop{\sffamily\footnotesize\noindent\hsize=6\tablemodule#}\hbox to 0.5\tablemodule{}&
	\hbox to 0.5\tablemodule{}\vtop{\sffamily\footnotesize\noindent\hsize=11\tablemodule#}\cr
	\color{gray75}\bfseries Vehicle type& \color{gray75}\bfseries Vehicle length (meters)& \color{gray75}\bfseries Capacity (customers per vehicle) \cr
	\noalign{\vskip 1mm} \hline \noalign{\vskip 2mm} 
	\color{gray50}Bi-articulated&
	\color{gray50}24&
	\color{gray50}240–270\cr
	\noalign{\vskip 2mm 
}	\color{gray50}Articulated&
	\color{gray50}18&
	\color{gray50}120–170\cr
	\noalign{\vskip 2mm 
}	\color{gray50}Elongated&
	\color{gray50}15&
	\color{gray50}90–120\cr
	\noalign{\vskip 2mm 
}	\color{gray50}Standard&
	\color{gray50}12&
	\color{gray50}60–80\cr
	\noalign{\vskip 2mm 
}	\color{gray50}Minibus&
	\color{gray50}6&
	\color{gray50}25–35\cr
	\noalign{\vskip 2mm 
}}
% @ guide/07-system-speed/expanding-corridor-capacity.src: lines 42-67: code points 1-9

\ifxetex
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	{  % group required to avoid fignote settings escaping
		\img{\hsize}{operations/assets/645895783def821971dbd2f94ea74ebbfb9879d5.jpeg}
		\fignote{10}{The 18-meter articulated vehicle has become standard in many BRT applications. Image of Hangzhou, China, BRT vehicle\label{chapter:system-speed-and-capacity:figure:the-18-meter-articulated-vehicle-has-become-standard-in-many-brt-applications-image-of-hangzhou-china-brt-vehicle}}{Kangming Xu (http:\slash{}\slash{}www.brtchina.org).}
	}
\fi

Increasingly, the 18-meter articulated vehicle is becoming the standard for BRT systems. The Curitiba system has utilized the larger bi-articulated vehicles, as have Mexico City’s Insurgentes Avenue and TransMilenio in Bogotá. There are several reasons for the current dominance of the articulated vehicles (160-customer capacity) over the bi-articulated vehicle (270-customer vehicle):\par
% @ guide/07-system-speed/expanding-corridor-capacity.src: lines 72-76: code points 1-0

\begin{itemize}
\item Large numbers of articulated vehicle orders have produced cost savings through economies-of-scale in manufacturing;% @ guide/07-system-speed/expanding-corridor-capacity.src: lines 76-78: code points 1-0
\item Currently only a few manufacturers offer a bi-articulated vehicle, thus limiting the power of competition during the bid process;% @ guide/07-system-speed/expanding-corridor-capacity.src: lines 78-80: code points 1-0
\item Heavier weight of bi-articulated vehicles reduces fuel efficiency and ability to accelerate rapidly;% @ guide/07-system-speed/expanding-corridor-capacity.src: lines 80-81: code points 1-0
\item Length of bi-articulated vehicles (24 meters) can create difficulties with regard to available length of right-of-way at stations.% @ guide/07-system-speed/expanding-corridor-capacity.src: lines 81-82: code points 1-35
\end{itemize}
% @ guide/07-system-speed/expanding-corridor-capacity.src: lines 76-82: code points 1-35

There may be instances where the operational and physical characteristics of a corridor would make a bi-articulated vehicle an appropriate choice. Chapter 20: Vehicles discusses vehicle technology options.\par
% @ guide/07-system-speed/expanding-corridor-capacity.src: lines 84-85: code points 1-106

As vehicle length increases, there can be a diminishing return in terms of delivered capacity. If sub-stop capacity is reached and vehicle congestion occurs, then the additional capacity may not be fully realized. Figure 7.11 gives an example of this effect for a given set of parameters.\par
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\ifxetex
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		\img{\hsize}{operations/assets/69e6de48bd488bf3391a0af2ef6002be017f1199.jpeg}
		\fignote{11}{Relationship between vehicle size and capacity. Graph\label{chapter:system-speed-and-capacity:figure:relationship-between-vehicle-size-and-capacity-graph}}{Steer Davies Greer.}
	}
\fi

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		\img{\hsize}{operations/assets/07837f8e476c892697b272d8b611047ffdb3237f.jpeg}
		\fignote{12}{The perception of a relatively empty busway next to heavily congested mixed-traffic lanes, as seen in this image of Quito.\label{chapter:system-speed-and-capacity:figure:the-perception-of-a-relatively-empty-busway-next-to-heavily-congested-mixed-traffic-lanes-as-seen-in-this-image-of-quito}}{Lloyd Wright}
	}
\fi

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		\img{\hsize}{operations/assets/24504a5310726dd78d294136068d8d246dc9b839.jpeg}
		\fignote{13}{Empty busways, as seen here in Hangzhou, may create political pressure to open up the lane to private vehicles.\label{chapter:system-speed-and-capacity:figure:empty-busways-as-seen-here-in-hangzhou-may-create-political-pressure-to-open-up-the-lane-to-private-vehicles}}{Karl Fjellstrom}
	}
\fi

While the vehicle size may optimize costs, very low frequencies can have a perverse effect. First, customer frequency translates into reliability, and this is one of the main drivers of transit use. Without sufficient frequency and reliability, customers may choose other options, and the system will lose ridership. Secondly, car drivers in traffic congestion will become frustrated from seeing an empty busway beside them. In turn, motorists will complain that the road is being under-utilized (Figures 7.12 and 7.13). Such complaints ultimately undermine political support for future busways. While a headway of a few minutes may not seem like a lot of time, a busway with a vehicle passing only every few minutes can appear to be empty most of the time. In Quito, pressure from motorist organizations led the national police to open up exclusive busway corridors to mixed traffic for a period of time in 2006. This conversion occurred despite the fact that each busway lane was moving three to four times the volume of customers as a mixed-traffic lane. Nevertheless, the perception of an empty busway next to heavily congested mixed-traffic lanes can create political difficulties.\par
% @ guide/07-system-speed/expanding-corridor-capacity.src: lines 99-110: code points 1-30

\subsection{3}{Adding Passing Lanes and Multiple Sub-Stops}
\label{volume:operations:chapter:system-speed-and-capacity:section:expanding-corridor-capacity:subSection:adding-passing-lanes-and-multiple-sub-stops}
% @ guide/07-system-speed/expanding-corridor-capacity.src: lines 112-193: code points 1-92

Measures such as vehicle size, vehicle-station interface, and doorway widths all contribute to higher-capacity and higher-speed systems. However, even together, these measures will likely only produce capacities around 9,000 pphpd. Thus, while systems such as those in Curitiba and Quito are high-quality BRT systems, their maximum corridor capacities are limited to this value.\par
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		\img{\hsize}{operations/assets/62c07b0b556faf5e1205f4dab0c53aa3ba82b782.jpeg}
		\fignote{14}{The use of multiple sub-stops and passing lanes helps minimize vehicle congestion in Guangzhou, China. Photo\label{chapter:system-speed-and-capacity:figure:the-use-of-multiple-sub-stops-and-passing-lanes-helps-minimize-vehicle-congestion-in-guangzhou-china-photo}}{ITDP.}
	}
\fi

A key innovation of Bogotá’s TransMilenio system was that more capacity and speed was obtained because at each station, instead of having just one sub-stop, there were multiple sub-stops (Figure 7.14). In some cases, a single TransMilenio station will host up to three sub-stops (Figure 7.15). Each sub-stop represents a different set of services or routes (e.g., local services versus limited-stop services or routes with a different final destination). As a result, TransMilenio is able to handle capacities up to 45,000 pphpd, and there are good indications that values as high as 50,000 pphpd or even higher are now possible with BRT. Guangzhou achieves throughput of 27,400 pphpd, and new systems under construction in Rio de Janeiro promise to achieve even higher throughput levels than Bogotá and Guangzhou.\par
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		\img{\hsize}{operations/assets/e772b5ca86d48dee3d4d4a753cedd6fc709e2f6a.jpeg}
		\fignote{15}{The use of multiple sub-stops, in this case three in each direction, has been one of the principal reasons Bogotá is able to achieve very high customer capacities. Photo\label{chapter:system-speed-and-capacity:figure:the-use-of-multiple-sub-stops-in-this-case-three-in-each-direction-has-been-one-of-the-principal-reasons-bogot-is-able-to-achieve-very-high-customer-capacities-photo}}{Akiris.}
	}
\fi

At any given point on TransMilenio’s corridors a vehicle passes every twenty seconds—a headway that would lead to significant saturation on a corridor without passing lanes. However, with passing lanes and multiple sub-stops and docking bays, the headway at \emphasis{each} docking bay tends to be around one minute. Guangzhou similarly achieves very high frequency, and is, with Bogotá, the highest vehicle frequency BRT in the world, averaging around one vehicle every eleven seconds in the peak direction and peak hour. The Brisbane Busway achieves peak-hour vehicle frequencies of one vehicle every fourteen seconds in a single direction.\par
% @ guide/07-system-speed/expanding-corridor-capacity.src: lines 134-140: code points 1-17

The presence of multiple sub-stops serves two distinct purposes. First, the multiple sub-stops permit many different types of services to operate from the same station, such as local services or limited-stop services. Each sub-stop represents a different set of services or routes. Second, the multiple sub-stops can dramatically reduce the saturation level at the stations. Since station saturation is typically the principal barrier to higher-capacity services, adding sub-stops is perhaps the cornerstone of any proposed system requiring higher capacity levels.\par
% @ guide/07-system-speed/expanding-corridor-capacity.src: lines 142-147: code points 1-40

As noted earlier, to maintain a high level of service, saturation levels should be 40 percent or below. If saturation is over that, a second lane and a second sub-stop are likely to be required. As saturation increases, more sub-stops will be needed. In order to maintain a saturation factor of less than 0.40, services at each sub-stop must be properly scheduled and spaced to limit congestion. A saturation factor of 0.40 corresponds to approximately 60 vehicles per hour, but the specific sub-stop demand can reduce or increase this value. If 18-meter articulated vehicles are utilized, then 60 vehicles per hour corresponds to an approximate capacity of 9,000 pphpd, and this figure is a general limit for a simple one-lane operation. Since a lane will begin to congest once seventy vehicles per hour per direction is reached, a second sub-stop is recommended whenever volumes exceed this level.\par
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\emphasis{Inclusion of Limited-Stop Services}\par
% @ guide/07-system-speed/expanding-corridor-capacity.src: lines 159-159: code points 1-36

\ifxetex
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		\img{\hsize}{operations/assets/1fc8bb893157582f8d4432f5116f77e0b019f093.jpeg}
		\fignote{16}{Shidajida BRT station in Guangzhou, at the midpoint underneath the access bridge, has one lane carrying 27,000 people and one vehicle every 10 seconds in a single direction during the peak hour. This system has the world’s highest BRT vehicle carrying frequency. Image\label{chapter:system-speed-and-capacity:figure:shidajida-brt-station-in-guangzhou-at-the-midpoint-underneath-the-access-bridge-has-one-lane-carrying-27000-people-and-one-vehicle-every-10-seconds-in-a-single-direction-during-the-peak-hour-this-system-has-the-worlds-highest-brt-vehicle-carrying-frequency-image}}{ITDP.}
	}
\fi

With the inclusion of overtaking lanes, to increase capacity further, limited-stop services are likely to be the most relevant tool for planners. These services do not add to station saturation. In addition, they have higher commercial speeds. This way, stations with the highest demands can have services go directly to them. The potential of “express services” is limited for practical purposes, as the higher the demand, the easier it is to provide limited-stop services. One lane can serve up to 720 articulated-vehicles per hour. If 50 percent of this is used, that would be equivalent to more than 50,000 pphpd. Section 6.7.2 discusses how to implement direct service patterns. In the case of TransMilenio, approximately 50 percent of vehicles serve these types of routes.\par
% @ guide/07-system-speed/expanding-corridor-capacity.src: lines 166-173: code points 1-51

\emphasis{Passing Lanes at Stations Only}\par
% @ guide/07-system-speed/expanding-corridor-capacity.src: lines 175-175: code points 1-32

The principal difficulty in including a passing lane is the impact on road space. Unless the corridor is highly saturated, passing lanes are usually not required beyond the stations. However, at station locations, the BRT system will occupy the width of the station plus two lanes in either direction, an approximate total of five lane widths for a station that serves both directions or a total of three lanes if the sub-stops for each direction are offset from each other. As discussed in Chapter 24: Intersections and Signal Control, if the BRT is in a section with intersections, narrowing mixed-traffic lanes will have no impact on the segment capacity if the station is at an appropriate distance from the intersections.\par
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\ifxetex
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		\img{\hsize}{operations/assets/dafac469d74533498296e868c36225a94b9f0884.jpeg}
		\fignote{17}{In order to accommodate passing lanes at stations on the proposed Dar es Salaam system, the road infrastructure was widened.\label{chapter:system-speed-and-capacity:figure:in-order-to-accommodate-passing-lanes-at-stations-on-the-proposed-dar-es-salaam-system-the-road-infrastructure-was-widened}}{ITDP.}
	}
\fi

Other options for accommodating passing lanes in express roadways include making property purchases for widening. In some BRT cities, such as Barranquilla, Colombia, plans called for the purchase of properties near station areas. The road infrastructure was widened in these areas in order to accommodate the passing lane. This same strategy has been employed at some stations in the BRT system in Dar es Salaam, Tanzania, as shown in Figure 7.17. The viability of property purchases for this purpose depends on local property costs, as well as the existence of a well-designed compensation program for property owners.\par
% @ guide/07-system-speed/expanding-corridor-capacity.src: lines 188-193: code points 1-92

\subsection{4}{Convoying}
\label{volume:operations:chapter:system-speed-and-capacity:section:expanding-corridor-capacity:subSection:convoying}
% @ guide/07-system-speed/expanding-corridor-capacity.src: lines 196-241: code points 1-0

In general, multiple sub-stops are coupled with passing lanes in order to allow vehicles to pass one another and thus readily access the appropriate docking bay. However, if there is insufficient space for passing lanes, there is still a way to utilize multiple sub-stops without a passing lane. Some of the benefits of separate sub-stops can be achieved through the “convoying” or “platooning” of vehicles. Convoys involve two or more vehicles operating along the busway in a closely bunched pack. In some respects, a convoy system is similar to an extended set of rail cars. The order of the vehicles is typically set so that the first vehicle stops at the far sub-stop, and the next vehicle stops at the subsequent one.\par
% @ guide/07-system-speed/expanding-corridor-capacity.src: lines 198-204: code points 1-102

A single lane operating with a single sub-stop per station can achieve a corridor capacity of approximately 9,000 pphpd. Convoys can increase capacity by around 50 percent to a maximum of 13,000 pphpd without any reduction in the level of service.\par
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Unfortunately, the convoying or platooning of vehicles is difficult to manage and control. The vehicles must enter the busway in the right order or there will be delays and backing up of vehicles. Further, since customer boardings will vary for different vehicles, the dwell times will also vary. Some vehicles may needlessly wait behind others that have a longer boarding process. Thus, in a convoy system the slowest vehicle will likely set the speed for the entire fleet. For these reasons, multiple sub-stops are best implemented through the provision of passing lanes at stations.\par
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Some conventional systems utilizing convoying have achieved corridor capacities over 20,000 pphpd. Both the Farrapos and the Assis Brasil bus corridors in Porto Alegre, Brazil, reach peak capacities of over 20,000 pphpd through convoying techniques. Nevertheless, the penalty for extending convoying to this level without reducing boarding and alighting times is a reduced level of service in terms of average speed.\par
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\ifxetex
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		\img{\hsize}{operations/assets/e54f7c6d954e9b6269c37b58c7bfb79e2e638d85.jpeg}
		\fignote{18}{Convoying allows the Porto Alegre system to achieve peak capacities of over 20,000 pphpd, but at the same time this technique can result in busway and station congestion, and therefore slow average vehicle speeds.\label{chapter:system-speed-and-capacity:figure:convoying-allows-the-porto-alegre-system-to-achieve-peak-capacities-of-over-20000-pphpd-but-at-the-same-time-this-technique-can-result-in-busway-and-station-congestion-and-therefore-slow-average-vehicle-speeds}}{Lloyd Wright.}
	}
\fi

Systems may operate as ordered convoys or non-ordered convoys. In an ordered convoy, the vehicles must approach the station in a set order so that the vehicles stop in the designated sub-stop. Signage at the station instructs customers which sub-stop corresponds with their intended route. To manage and control the order of the vehicles entering the busway, a control center in conjunction with automatic vehicle locating (AVL) technology will be essential. Communications between the control center and the drivers allow each vehicle to adjust its position in order to enter the busway at the right moment.\par
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In a non-ordered convoy, the vehicles approach the station in any order, depending on the timing of each vehicle’s entry into the main busway. In this case, customers will not know at which sub-stop their intended route will stop. Even if announcements give customers a brief warning, there is still likely to be some confusion. This situation implies that customers may be running from one end of the station to the other in search of their intended vehicle, or as mentioned previously, will have to flag down the bus to make a second stop at the station in order to pick them up.\par
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\section{6}{Optimizing the Station-to-Vehicle Interface}
\label{volume:operations:chapter:system-speed-and-capacity:section:optimizing-the-station-to-vehicle-interface}
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\quotation{Let every man praise the bridge that carries him over.}{English proverb}
% @ guide/07-system-speed/optimizing-the-station-to-vehicle-interface.src: lines 3-4: code points 1-17

Besides increasing the vehicle size, frequency, and number of sub-stops, there are several elements of the station-to-vehicle interface that can be improved to reduce boarding and alighting times, therefore reducing station saturation and increasing corridor capacity.\par
% @ guide/07-system-speed/optimizing-the-station-to-vehicle-interface.src: lines 6-8: code points 1-61

\ifxetex
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	{  % group required to avoid fignote settings escaping
		\img{\hsize}{operations/assets/fc7889e8e48ebf18828dca6b24888c9166c173a9.jpeg}
		\fignote{19}{Curitiba’s innovations with station design and the station-to-vehicle interface did much to make BRT a high-capacity option. Photo\label{chapter:system-speed-and-capacity:figure:curitibas-innovations-with-station-design-and-the-station-to-vehicle-interface-did-much-to-make-brt-a-high-capacity-option-photo}}{Volvo.}
	}
\fi

\subsection{1}{Platform-Level Boarding}
\label{volume:operations:chapter:system-speed-and-capacity:section:optimizing-the-station-to-vehicle-interface:subSection:platform-level-boarding}
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		\img{\hsize}{operations/assets/9eed0c56cce5ebfcb4224856afe4e7353a33973f.jpeg}
		\fignote{20}{With well-designed infrastructure and adequate driver training, such as in Ahmedabad, India,BRT systems can reduce the boarding gap to under 10 centimeters\label{chapter:system-speed-and-capacity:figure:with-well-designed-infrastructure-and-adequate-driver-training-such-as-in-ahmedabad-indiabrt-systems-can-reduce-the-boarding-gap-to-under-10-centimeters}}{Shreya Gadepalli}
	}
\fi

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		\img{\hsize}{operations/assets/0fa2bfd43df041681aae2e1a68f4cb3c6d63a220.jpeg}
		\fignote{21}{Reducing the borad gap, as seen here in Bogotá, Colombia, allows customers to safely board and alight faster, increasing system capacity.\label{chapter:system-speed-and-capacity:figure:reducing-the-borad-gap-as-seen-here-in-bogot-colombia-allows-customers-to-safely-board-and-alight-faster-increasing-system-capacity}}{Lloyd Wright.}
	}
\fi

To reduce boarding and alighting times, most state-of-the-art BRT systems have introduced platform-level boarding. With platform-level boarding, the docking bay platform is designed to be the same height as the vehicle floor. Besides fast boarding and alighting, it also allows easier access for persons in wheelchairs, parents with strollers, young children, and the elderly.\par
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There are currently two different types of platform-level boarding techniques. In one case, a gap exists between the platform and the vehicle. The gap may range from approximately 4 to 10 centimeters, depending on the accuracy of the vehicle-alignment process (Figures 7.20 and 7.21). Such a boarding gap is comparable to or smaller than the gap present on many rail-based systems. Alternatively, a vehicle can employ a boarding bridge that physically connects the vehicle to the platform, thereby eliminating the gap completely. The boarding bridge consists of a flip-down ramp that is attached to the vehicle’s doors or one that extends from the bus upon docking. As the doors open, the boarding bridge is released and covers the gap between the vehicle and the platform (Figures 7.22 and 7.23).\par
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		\img{\hsize}{operations/assets/83d0641382d03478e5120c70051c561fa71842b6.jpeg}
		\fignote{22}{System in Curitiba, Brazil, utilize a boarding bridge between the vehicle and platform to facilitate easy boarding\label{chapter:system-speed-and-capacity:figure:system-in-curitiba-brazil-utilize-a-boarding-bridge-between-the-vehicle-and-platform-to-facilitate-easy-boarding}}{Volvo}
	}
\fi

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		\img{\hsize}{operations/assets/4a76a16253783f97bb20a2ac5499fa6fd9e3ca93.jpeg}
		\fignote{23}{Even bus approach to station being faster, using boarding bridges, as seen here in Guayaquil, Ecuador, usually increases dead time, while certainly decreases boarding time per passenger.\label{chapter:system-speed-and-capacity:figure:even-bus-approach-to-station-being-faster-using-boarding-bridges-as-seen-here-in-guayaquil-ecuador-usually-increases-dead-time-while-certainly-decreases-boarding-time-per-passenger}}{Lloyd Wright.}
	}
\fi

Both techniques, gap entry and boarding bridge entry, have their advantages and disadvantages. Cities such as Curitiba and Quito have experienced much success with boarding bridges. A typical boarding bridge is 40 to 50 centimeters in width, meaning that the vehicle only needs to align within about 35 to 45 centimeters of the platform (Figure 7.24). Thus, there is much more room for error using the boarding bridge.\par
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		\img{\hsize}{operations/assets/2adc09f2d1ad5e7bb96fa8968266711c09a62373.jpeg}
		\fignote{24}{The presence of the boarding bridge means that the driver does not need to be as precise docking at the platform. In turn, this can significantly reduce the time lost at stations. Image\label{chapter:system-speed-and-capacity:figure:the-presence-of-the-boarding-bridge-means-that-the-driver-does-not-need-to-be-as-precise-docking-at-the-platform-in-turn-this-can-significantly-reduce-the-time-lost-at-stations-image}}{Lloyd Wright.}
	}
\fi

The boarding bridge also provides boarding and alighting customers with greater confidence in placing their steps. The confidence means that customers will not have to look down at a gap to judge safe foot placement. Instead, customers confidently march forward. The small act of looking down slows each person’s boarding and alighting time. While this lost time seems small on a per customer basis, the cumulative effect across all customers can be significant. The added customer confidence with the boarding bridge also means that two people can board or alight side by side. When a gap is present, customers are less likely to board simultaneously. The uncertainty imposed by a gap means customers are less likely to handle both the placement of the foot and the distance beside another customer at the interface point. A boarding bridge also is significantly more user-friendly to customers with physical disabilities, wheelchairs, and strollers.\par
% @ guide/07-system-speed/optimizing-the-station-to-vehicle-interface.src: lines 53-62: code points 1-14

Despite these benefits, the boarding bridge does bring with it a few disadvantages. The added cost of the boarding plate and the pneumatic system to operate it entails a modest increase in vehicle costs, as well as an increase in maintenance costs. As a moving part, the boarding bridge also introduces additional maintenance issues and the potential for malfunction. There is also one aspect of the boarding bridge that does not hold a time advantage. The deployment of the bridge itself takes about 1.5 seconds. Likewise, the retrieval of the boarding bridge at departure also requires about 1.5 seconds. While this deployment and retrieval roughly coincide with the opening and closing of the doors, they introduce a slight delay to the boarding and alighting process. However, the overall efficiency advantages of the boarding bridge tend to compensate for the deployment and retrieval time.\par
% @ guide/07-system-speed/optimizing-the-station-to-vehicle-interface.src: lines 64-72: code points 1-57

\subsection{2}{Fare Collection}
\label{volume:operations:chapter:system-speed-and-capacity:section:optimizing-the-station-to-vehicle-interface:subSection:fare-collection}
% @ guide/07-system-speed/optimizing-the-station-to-vehicle-interface.src: lines 74-172: code points 1-36

In many conventional bus services, the driver is responsible for the collection of fares, as well as driving the vehicle, and customers are only allowed to enter through the front door. Thus, on-board fare collection means that boarding time is largely determined by the fare collection activity. If the fare collection process is slow, the whole public transport service is slow. Typically, customers take from two to four seconds just to pay the driver. If drivers also need to give customers change manually, this increases to fifteen seconds. Once customer flows reach a certain point, the delays and time loss associated with on-board fare collection become a significant system liability.\par
% @ guide/07-system-speed/optimizing-the-station-to-vehicle-interface.src: lines 76-82: code points 1-68

\ifxetex
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\else
	{  % group required to avoid fignote settings escaping
		\img{\hsize}{operations/assets/bb1680f1e403f13dbfe6282b920b3acf608049eb.jpeg}
		\fignote{25}{On-board fare collection and fare verification dramatically slows customer throughputs, as evidenced here in Harbin, China. Photo\label{chapter:system-speed-and-capacity:figure:on-board-fare-collection-and-fare-verification-dramatically-slows-customer-throughputs-as-evidenced-here-in-harbin-china-photo}}{Lloyd Wright.}
	}
\fi

Most BRT systems since Curitiba have instituted external or off-board fare collection and fare verification. Customers pay their fare prior to entering the station, and then have their fare verified as they pass the entry turnstile or gate. Boarding and alighting can happen from all doors at once, and there is no delay in boarding and alighting related to the fare collection and fare verification. Pre-board fare collection and verification can reduce boarding times from 3 seconds per customer to 0.3 seconds per customer. In turn, the reduction in station dwell time greatly reduces vehicle congestion at the docking bay.\par
% @ guide/07-system-speed/optimizing-the-station-to-vehicle-interface.src: lines 87-93: code points 1-4

The introduction of contactless smart cards and other modern payment systems can reduce on-board payment to two seconds per customer. Systems such as the Seoul busway make use of on-board fare collection using smart card technology. However, any time the driver is responsible for verifying fares, the speed of the service will be highly compromised, particularly if there is a large volume of customers. In the case of the Seoul busway system, customers must remember to swipe their smart cards both upon entering and exiting the vehicle. Delays can occur simply if a person enters the vehicle and must search through his or her belongings to find the fare card.\par
% @ guide/07-system-speed/optimizing-the-station-to-vehicle-interface.src: lines 95-101: code points 1-44

In some cities, on-board collecting devices have been installed at all doors, and the average time for boarding and alighting has been reduced further. On the Geary Boulevard corridor in San Francisco, USA, the boarding times are 1.2 seconds per customer. However, these times are still several times higher than boarding times for systems with off-board fare collection.\par
% @ guide/07-system-speed/optimizing-the-station-to-vehicle-interface.src: lines 103-106: code points 1-63

\ifxetex
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\else
	{  % group required to avoid fignote settings escaping
		\img{\hsize}{operations/assets/bb1680f1e403f13dbfe6282b920b3acf608049eb.jpeg}
		\fignote{26}{The Seoul busway system uses smart card technology for fare collection.\label{chapter:system-speed-and-capacity:figure:the-seoul-busway-system-uses-smart-card-technology-for-fare-collection}}{Lloyd Wright.}
	}
\fi

\ifxetex
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\else
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		\img{\hsize}{operations/assets/e228ef144ff28d5968f42f722cc991b2cc3833cf.jpeg}
		\fignote{27}{However, in Seoul, the on-board nature of the fare verification means that dwell times are negatively affected. A single customer searching through personal effects for his or her smart card can create a significant delay.\label{chapter:system-speed-and-capacity:figure:however-in-seoul-the-on-board-nature-of-the-fare-verification-means-that-dwell-times-are-negatively-affected-a-single-customer-searching-through-personal-effects-for-his-or-her-smart-card-can-create-a-significant-delay}}{Lloyd Wright.}
	}
\fi

In some bus systems, conductors sell tickets on board. While such an arrangement allows customers to board quickly and then pay once the vehicle is moving, it may introduce other forms of delay. For example, in some bus systems in India, it is common for the driver to halt the bus to make sure the conductor has finished selling tickets to all of the customers on the bus before it reaches the next stop.\par
% @ guide/07-system-speed/optimizing-the-station-to-vehicle-interface.src: lines 114-117: code points 1-96

In addition, if the conductor remains in a fixed position on the bus, and boarding happens through multiple doors, customers must make their way to the conductor, a potentially unpleasant ordeal if the vehicle is crowded. Some systems employ a reservoir area within the vehicle to hold customers while they go through the fare payment and verification process (Figure 7.28) at a turnstile. However, in this technique, the boarding is just through one door that is always less efficient than all-door boarding, especially when using large buses. In Brazil, the reservoir area is commonly small to prevent fare evasion (by getting off the bus through the same door). The first three or four customers need one second to board; the remaining need three to five seconds.\par
% @ guide/07-system-speed/optimizing-the-station-to-vehicle-interface.src: lines 119-126: code points 1-27

\ifxetex
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	{  % group required to avoid fignote settings escaping
		\img{\hsize}{operations/assets/b8d2f8fa5a0d2494e193a663a1cb5babd5800c3b.jpeg}
		\fignote{28}{In Brazil, a “reservoir” space is provided within the vehicle where customers line up to verify their fare. With the use of this reservoir, the vehicle is able to proceed even before all customers have verified their fare.\label{chapter:system-speed-and-capacity:figure:in-brazil-a-reservoir-space-is-provided-within-the-vehicle-where-customers-line-up-to-verify-their-fare-with-the-use-of-this-reservoir-the-vehicle-is-able-to-proceed-even-before-all-customers-have-verified-their-fare}}{The Municipality of Goiânia.}
	}
\fi

With on-board fare collection and verification, alighting is usually faster than boarding. Typically, alighting times are approximately 70 percent of boarding times. In the case of off-board fare collection and verification, when vehicles are not crowded, there is no significant difference between boarding and alighting times.\par
% @ guide/07-system-speed/optimizing-the-station-to-vehicle-interface.src: lines 132-135: code points 1-20

Another method to avoid on-board fare collection and reduce boarding delays is to employ a proof-of-payment; customers must validate their own tickets that they purchase at shops and kiosks. Enforcement is then the responsibility of the police or contracted security personnel. Many European light rail systems utilize such a system.\par
% @ guide/07-system-speed/optimizing-the-station-to-vehicle-interface.src: lines 137-140: code points 1-14

Off-board payment collection is not necessarily the only way to reduce boarding and alighting times, but there are institutional reasons why this approach is generally more successful in the developing-country context. In particular, off-board fare collection and verification enhances the transparency of the process of collecting the fare revenues. When customers pay on board, and do not have to pass through a turnstile, there is no clear count of how many customers boarded the vehicle. Off-board fare sales to a third party make it easier to separate the fare collection process from bus operations. By having an open and transparent fare collection system, there is less opportunity for circumstances in which individuals withhold funds. This separation of responsibilities has regulatory and operational advantages that are discussed in more detail below. Further, by removing the handling of cash on board, incidents of on-board robbery are reduced.\par
% @ guide/07-system-speed/optimizing-the-station-to-vehicle-interface.src: lines 142-150: code points 1-108

Off-board payment also facilitates seamless transfers within the system. In addition, enclosed, controlled stations give the system another level of security, as security personnel can better protect the stations, thus discouraging theft and other undesirable activities. Off-board payment is also more comfortable than juggling change within a moving vehicle.\par
% @ guide/07-system-speed/optimizing-the-station-to-vehicle-interface.src: lines 152-155: code points 1-45

The main disadvantage to off-board fare collection is the need to construct and operate off-board fare facilities. Fare vending machines, fare sales booths, fare verification devices, and turnstiles all require both investment and physical space. In a BRT system with limited physical space for stations in a center median, accommodating the fare collection and verification infrastructure can be a challenge.\par
% @ guide/07-system-speed/optimizing-the-station-to-vehicle-interface.src: lines 157-160: code points 1-93

\ifxetex
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\else
	{  % group required to avoid fignote settings escaping
		\img{\hsize}{operations/assets/1e02f3bdfa7b82bb82f51e208701aec656241b7d.jpeg}
		\fignote{29}{Cost-benefit analysis of external fare collection. Graph\label{chapter:system-speed-and-capacity:figure:cost-benefit-analysis-of-external-fare-collection-graph}}{Steer Davies Greer.}
	}
\fi

There is no one precise point at which a system’s capacity will determine if on-board or off-board fare collection is more cost effective. Much depends on demand figures from individual stations, physical configurations of stations, and average labor costs. However, the advantage of off-board payment clearly increases as the level of boardings and alightings at the station increases and is part of the BRT basics of \emphasis{The BRT Standard}. In Goiânia, Brazil, the local public transport agency estimates that an off-board fare system is cost justified when the system capacity reaches 2,500 pphpd. The development of a cost-benefit analysis may help determine this capacity point, provided the data is available. Figure 7.29 provides an example of this type of analysis.\par
% @ guide/07-system-speed/optimizing-the-station-to-vehicle-interface.src: lines 165-172: code points 1-36

\subsection{3}{Doorways}
\label{volume:operations:chapter:system-speed-and-capacity:section:optimizing-the-station-to-vehicle-interface:subSection:doorways}
% @ guide/07-system-speed/optimizing-the-station-to-vehicle-interface.src: lines 175-320: code points 1-18

All the efforts applied to vehicle size, station design, and fare collection can be in vain if the vehicle’s doorways inhibit smooth customer flows. With too few doors, systems experience bottlenecks due to conflicts between entering and exiting customers that increase the total boarding and alighting time.\par
% @ guide/07-system-speed/optimizing-the-station-to-vehicle-interface.src: lines 177-180: code points 1-5

The most successful BRT systems have employed multiple wide doorways dispersed along the length of the vehicle to ensure that platform bottlenecks are avoided. Having multiple wide doors accounts for a total of 3 points in \emphasis{The BRT Standard}. These systems provide at least two separate sets of doors on 12-meter vehicles and three separate doors on 18-meter articulated vehicles. Each double door should be at least 1 meter wide, allowing two people to enter or exit the vehicle simultaneously. Table 7.13 compares observed boarding and alighting times for different combinations of doorway and platform configurations.\par
% @ guide/07-system-speed/optimizing-the-station-to-vehicle-interface.src: lines 182-187: code points 1-113

\tabletitle{13}{Observed Boarding and Alighting Times for Different Configurations}
\label{chapter:system-speed-and-capacity:table:observed-boarding-and-alighting-times-for-different-configurations}
\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=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=5\tablemodule#}\hbox to 0.5\tablemodule{}&
	\hbox to 0.5\tablemodule{}\vtop{\sffamily\footnotesize\noindent\hsize=5\tablemodule#}\cr
	\color{gray75}\bfseries \highlight{Configuration characteristics}& \color{gray75}\bfseries & \color{gray75}\bfseries & \color{gray75}\bfseries & \color{gray75}\bfseries \highlight{Boarding and alighting times}& \color{gray75}\bfseries  \cr
	\noalign{\vskip 1mm} \hline \noalign{\vskip 2mm} 
	\color{gray50}Fare collection method&
	\color{gray50}Doorway width (meters)&
	\color{gray50}Stairway boarding or level boarding&
	\color{gray50}Vehicle floor height&
	\color{gray50}Observed; boarding time;&
	\color{gray50}Observed alighting time\cr
	\noalign{\vskip 2mm 
}	\color{gray50}On-board, manually by driver&
	\color{gray50}0.6&
	\color{gray50}Stairway&
	\color{gray50}High&
	\color{gray50}3.01&
	\color{gray50}NA\cr
	\noalign{\vskip 2mm 
}	\color{gray50}On-board, contactless smart card (no turnstile)&
	\color{gray50}0.6&
	\color{gray50}Stairway&
	\color{gray50}High&
	\color{gray50}2.02&
	\color{gray50}NA\cr
	\noalign{\vskip 2mm 
}	\color{gray50}Off-board&
	\color{gray50}0.6&
	\color{gray50}Stairway&
	\color{gray50}High&
	\color{gray50}23&
	\color{gray50}1.53\cr
	\noalign{\vskip 2mm 
}	\color{gray50}Off-board&
	\color{gray50}0.6&
	\color{gray50}Stairway&
	\color{gray50}Low&
	\color{gray50}1.5&
	\color{gray50}1.2\cr
	\noalign{\vskip 2mm 
}	\color{gray50}Off-board&
	\color{gray50}1.1&
	\color{gray50}Stairway&
	\color{gray50}High&
	\color{gray50}1.5&
	\color{gray50}1.0\cr
	\noalign{\vskip 2mm 
}	\color{gray50}Off-board&
	\color{gray50}1.1&
	\color{gray50}Stairway&
	\color{gray50}Low&
	\color{gray50}1.1&
	\color{gray50}0.9\cr
	\noalign{\vskip 2mm 
}	\color{gray50}Off-board&
	\color{gray50}1.1&
	\color{gray50}Level&
	\color{gray50}High&
	\color{gray50}0.751&
	\color{gray50}0.51\cr
	\noalign{\vskip 2mm 
}}
% @ guide/07-system-speed/optimizing-the-station-to-vehicle-interface.src: lines 189-253: code points 1-9

1. Colombia, Mexico\par
% @ guide/07-system-speed/optimizing-the-station-to-vehicle-interface.src: lines 255-255: code points 1-19

2. China\par
% @ guide/07-system-speed/optimizing-the-station-to-vehicle-interface.src: lines 257-257: code points 1-8

3. Brazil\par
% @ guide/07-system-speed/optimizing-the-station-to-vehicle-interface.src: lines 259-259: code points 1-9

NA – Not available\par
% @ guide/07-system-speed/optimizing-the-station-to-vehicle-interface.src: lines 261-261: code points 1-18

Doorway efficiency can also be closely tied to the interior design of the vehicle. The arrangement of the vehicle interior should provide adequate circulation space near the doors. In addition, some systems attempt to mitigate the conflict between boarding and alighting customers by designating some doorways as entry only and others as exit only. Curitiba utilizes this technique in some of its stations. This directional designation can improve boarding and alighting efficiency, but it can also cause customer confusion unless doorways are clearly marked.\par
% @ guide/07-system-speed/optimizing-the-station-to-vehicle-interface.src: lines 263-268: code points 1-45

\ifxetex
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	{  % group required to avoid fignote settings escaping
		\img{\hsize}{operations/assets/e17218859c235ad6728e509fa0332f10fb792526.jpeg}
		\fignote{30}{The overcrowding of vehicles results in delayed boarding and alighting due to customer congestion around the doorway (Bogotá). Image\label{chapter:system-speed-and-capacity:figure:the-overcrowding-of-vehicles-results-in-delayed-boarding-and-alighting-due-to-customer-congestion-around-the-doorway-bogot-image}}{Carlosfelipe Pardo.}
	}
\fi

The initial design of the Jakarta BRT system limited the system’s capacity due to the decision to utilize only a single doorway for both boarding and alighting. As a solution to its capacity constraints, TransJakarta elected to increase its vehicle fleet. However, only a small number of the additional vehicles actually helped increase capacity before vehicle queuing at the stations dropped the level of service down to unacceptable levels. Table 7.14 presents potential solutions to TransJakarta’s capacity problems. Shifting toward an articulated vehicle with multiple wide doorways would add the most capacity to the existing system, the analysis showed. TransJakarta has subsequently retrofitted stations and buses with additional doors and purchased larger vehicles.\par
% @ guide/07-system-speed/optimizing-the-station-to-vehicle-interface.src: lines 273-280: code points 1-36

\tabletitle{14}{Scenarios for Improving TransJakarta’s Capacity}
\label{chapter:system-speed-and-capacity:table:scenarios-for-improving-transjakartas-capacity}
\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=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=5\tablemodule#}\hbox to 0.5\tablemodule{}&
	\hbox to 0.5\tablemodule{}\vtop{\sffamily\footnotesize\noindent\hsize=5\tablemodule#}\cr
	\color{gray75}\bfseries Scenario& \color{gray75}\bfseries Average boarding time (seconds)& \color{gray75}\bfseries Dwell time (seconds)& \color{gray75}\bfseries Average speed (kph)& \color{gray75}\bfseries Capacity (pphpd)& \color{gray75}\bfseries Required fleet size (vehicles) \cr
	\noalign{\vskip 1mm} \hline \noalign{\vskip 2mm} 
	\color{gray50}Present situation&
	\color{gray50}2.5&
	\color{gray50}45&
	\color{gray50}17&
	\color{gray50}2,700&
	\color{gray50}60\cr
	\noalign{\vskip 2mm 
}	\color{gray50}Improving boarding&
	\color{gray50}1.7&
	\color{gray50}35&
	\color{gray50}19&
	\color{gray50}3,700&
	\color{gray50}56\cr
	\noalign{\vskip 2mm 
}	\color{gray50}Vehicle with 2 doors&
	\color{gray50}0.5&
	\color{gray50}22&
	\color{gray50}21&
	\color{gray50}6,000&
	\color{gray50}51\cr
	\noalign{\vskip 2mm 
}	\color{gray50}Articulated vehicle with 4 doors&
	\color{gray50}0.3&
	\color{gray50}18&
	\color{gray50}23&
	\color{gray50}9,600&
	\color{gray50}26\cr
	\noalign{\vskip 2mm 
}}
% @ guide/07-system-speed/optimizing-the-station-to-vehicle-interface.src: lines 282-318: code points 1-9

Source: ITDP, 2006\par
% @ guide/07-system-speed/optimizing-the-station-to-vehicle-interface.src: lines 320-320: code points 1-18

\subsection{4}{Vehicle Acceleration and Deceleration}
\label{volume:operations:chapter:system-speed-and-capacity:section:optimizing-the-station-to-vehicle-interface:subSection:vehicle-acceleration-and-deceleration}
% @ guide/07-system-speed/optimizing-the-station-to-vehicle-interface.src: lines 322-367: code points 1-84

The time required for a vehicle to approach and then accelerate away from a docking bay also has an impact on station saturation and system speeds. If conditions require a slow, careful approach to the stations, overall speeds and travel times will suffer. The time consumed in the deceleration and acceleration process is affected by the following factors:\par
% @ guide/07-system-speed/optimizing-the-station-to-vehicle-interface.src: lines 324-328: code points 1-0

\begin{itemize}
\item Type of vehicle-platform interface;% @ guide/07-system-speed/optimizing-the-station-to-vehicle-interface.src: lines 328-329: code points 1-0
\item Use of docking technology;% @ guide/07-system-speed/optimizing-the-station-to-vehicle-interface.src: lines 329-330: code points 1-0
\item Vehicle weight and engine capacity;% @ guide/07-system-speed/optimizing-the-station-to-vehicle-interface.src: lines 330-331: code points 1-0
\item Type of road surface;% @ guide/07-system-speed/optimizing-the-station-to-vehicle-interface.src: lines 331-332: code points 1-0
\item Presence of nearby at-grade pedestrian crossings.% @ guide/07-system-speed/optimizing-the-station-to-vehicle-interface.src: lines 332-332: code points 1-55
\end{itemize}
% @ guide/07-system-speed/optimizing-the-station-to-vehicle-interface.src: lines 328-332: code points 1-55

The vehicle deceleration time is greatly influenced by the closeness in docking required. Manual alignment generates variability in docking distances that can be improved somewhat through the use of optical targets for drivers along the face of the station. Mirrors can also improve the accuracy of manual targeting. Some other ways to help alignment include Kassel kerbs and flexible rubber lining the platform that allow the bus to come close to the station without damaging the bus or the station. All of the largest BRT systems in the world use manual alignment.\par
% @ guide/07-system-speed/optimizing-the-station-to-vehicle-interface.src: lines 334-339: code points 1-42

Alternatively, there are automatic docking technologies that try to increase the speed and accuracy of vehicle-to-platform alignment. Mechanical, optical, and magnetic docking technologies can all be applied for this purpose. In each of these cases, the vehicle is automatically guided into platform position without any intervention from the driver.\par
% @ guide/07-system-speed/optimizing-the-station-to-vehicle-interface.src: lines 341-344: code points 1-41

Mechanical guideway systems, such as those utilized in Adelaide, Australia; Essen, Germany; Leeds, England; and Nagoya, Japan, physically align the vehicle to the station through a fixed roller attached to the vehicle. In these cities, the fixed guideway is utilized both at stations and along the busway. However, a city could elect to only utilize the mechanical guidance at stations.\par
% @ guide/07-system-speed/optimizing-the-station-to-vehicle-interface.src: lines 346-349: code points 1-80

\ifxetex
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	{  % group required to avoid fignote settings escaping
		\img{\hsize}{operations/assets/0ab1679cbaade191917093b23604a0bd6883c809.jpeg}
		\fignote{31}{When operating properly, the optical guidance system employed in Las Vegas, Nevada, USA, can align the vehicle within a few centimeters of the platform.\label{chapter:system-speed-and-capacity:figure:when-operating-properly-the-optical-guidance-system-employed-in-las-vegas-nevada-usa-can-align-the-vehicle-within-a-few-centimeters-of-the-platform}}{NBRTI.}
	}
\fi

Optical docking systems operate through the interaction between an on-board camera and a visual indicator embedded in the busway. Software within the on-board guidance system then facilitates the automated steering of the vehicle. The Las Vegas, Nevada, USA, MAX system has attempted to make use of this type of technology (Figure 7.31). Problems have occurred, though, due to the inability of the optical reader to function properly when the roadway is wet.\par
% @ guide/07-system-speed/optimizing-the-station-to-vehicle-interface.src: lines 354-358: code points 1-33

A magnetic guidance system works on a similar principle to that of an optical system, but with magnetic materials placed in the roadway as the location indicator. The Philaeus bus, as utilized in the Eindhoven, Netherlands, BRT system, is capable of magnetic guidance.\par
% @ guide/07-system-speed/optimizing-the-station-to-vehicle-interface.src: lines 360-362: code points 1-57

Optical and magnetic guidance systems produce a highly precise degree of docking. However, due to current limitations with these technologies and their software, required deceleration and acceleration speeds can actually be slightly less than manual techniques. Further, the added hardware and software costs of an optically automated system can push vehicle costs well over US\$1 million each.\par
% @ guide/07-system-speed/optimizing-the-station-to-vehicle-interface.src: lines 364-367: code points 1-84

\subsection{5}{Station Platform}
\label{volume:operations:chapter:system-speed-and-capacity:section:optimizing-the-station-to-vehicle-interface:subSection:station-platform}
% @ guide/07-system-speed/optimizing-the-station-to-vehicle-interface.src: lines 370-381: code points 1-0

The size and layout of the station platform will have a discernible impact on system capacity and efficiency. In some systems, platform size can even be the principal constraint on overall capacity. For many of the London Tube lines, it is the relatively small platform width that ultimately determines the total possible customer volumes.\par
% @ guide/07-system-speed/optimizing-the-station-to-vehicle-interface.src: lines 372-375: code points 1-26

The determination of the optimum platform size is based on the number of customers waiting to board and alight. If the platform hosts two service directions along one another, then the sum capacity requirements of both directions must be factored into platform sizing. Chapter 25: Stations details the calculation of platform sizing.\par
% @ guide/07-system-speed/optimizing-the-station-to-vehicle-interface.src: lines 377-381: code points 1-0

\section{7}{Route Distribution among Multiple Sub-Stops}
\label{volume:operations:chapter:system-speed-and-capacity:section:route-distribution-among-multiple-sub-stops}
% @ guide/07-system-speed/route-distribution-among-multiple-sub-stops.src: lines 3-33: code points 1-0

\quotation{Even if you are on the right track, you will get run over if you just stand there.}{Will Rogers, social commentator and humorist, 1879–1935}
% @ guide/07-system-speed/route-distribution-among-multiple-sub-stops.src: lines 5-6: code points 1-57

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	{  % group required to avoid fignote settings escaping
		\img{\hsize}{operations/assets/1ff1b32c62af4724f3f02110050ac2cf8605c244.jpeg}
		\fignote{32}{Platform order based on geographic proximity of routes. Image\label{chapter:system-speed-and-capacity:figure:platform-order-based-on-geographic-proximity-of-routes-image}}{ITDP.}
	}
\fi

Multiple sub-stops imply the existence of multiple services stopping at a single station. Which routes should be grouped near one another, and which routes can be separated by a longer walk? The guiding principle should be customer convenience. Ideally, the right distribution of routes along the sub-stops should minimize the walking distance covered by the largest number of customers. Thus, the most common transfers should be grouped together. This philosophy will not only improve customer convenience, but it will also improve overall station capacity. If large numbers of customers are forced to crisscross the length of the platform area, then customer congestion will ensue. This congestion can increase the overall trip time for the customer, thereby having a negative impact on the overall performance of the corridor. Often, the greatest efficiency in station transfers can be gained by placing together routes that have destinations in relative geographic proximity to one another.\par
% @ guide/07-system-speed/route-distribution-among-multiple-sub-stops.src: lines 10-19: code points 1-48

\ifxetex
	% disabled for now
\else
	{  % group required to avoid fignote settings escaping
		\img{\hsize}{operations/assets/b003f47e78845cef30288000f4fd195c4247acdf.jpeg}
		\fignote{33}{Platform order based on service clustering. Image\label{chapter:system-speed-and-capacity:figure:platform-order-based-on-service-clustering-image}}{ITDP.}
	}
\fi

If frequencies are sufficiently spaced, some routes can even share the same docking bay. For example, a local service and a limited-stop service could share the same docking bay if the frequency levels make simultaneous arrivals unlikely. However, in such instances, sufficient space should be reserved for a vehicle to wait behind the docking bay, in case both routes arrive consecutively. The advantage of a shared docking bay is that customers changing from a local service to a limited-stop service (or vice versa) are not forced to walk to a different platform area. But if different routes share a docking bay, the risk of customer confusion increases. While route numbering, color coding, platform display messages, and platform audio announcements can all help minimize such confusion, some customers may unwittingly board the wrong vehicle.\par
% @ guide/07-system-speed/route-distribution-among-multiple-sub-stops.src: lines 24-33: code points 1-0

