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Intelligent Transportation Systems in Headway-Based Bus Service (2021)

Chapter: Chapter 3 - Survey Results

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Suggested Citation:"Chapter 3 - Survey Results." National Academies of Sciences, Engineering, and Medicine. 2021. Intelligent Transportation Systems in Headway-Based Bus Service. Washington, DC: The National Academies Press. doi: 10.17226/26163.
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Suggested Citation:"Chapter 3 - Survey Results." National Academies of Sciences, Engineering, and Medicine. 2021. Intelligent Transportation Systems in Headway-Based Bus Service. Washington, DC: The National Academies Press. doi: 10.17226/26163.
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Suggested Citation:"Chapter 3 - Survey Results." National Academies of Sciences, Engineering, and Medicine. 2021. Intelligent Transportation Systems in Headway-Based Bus Service. Washington, DC: The National Academies Press. doi: 10.17226/26163.
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Suggested Citation:"Chapter 3 - Survey Results." National Academies of Sciences, Engineering, and Medicine. 2021. Intelligent Transportation Systems in Headway-Based Bus Service. Washington, DC: The National Academies Press. doi: 10.17226/26163.
×
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Suggested Citation:"Chapter 3 - Survey Results." National Academies of Sciences, Engineering, and Medicine. 2021. Intelligent Transportation Systems in Headway-Based Bus Service. Washington, DC: The National Academies Press. doi: 10.17226/26163.
×
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Suggested Citation:"Chapter 3 - Survey Results." National Academies of Sciences, Engineering, and Medicine. 2021. Intelligent Transportation Systems in Headway-Based Bus Service. Washington, DC: The National Academies Press. doi: 10.17226/26163.
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20 Survey Results This chapter presents select results of the survey of transit agencies about their use of ITS in headway-based bus operations. Appendix B contains additional survey estimates for a more granular review of the survey data. Screening Questions Table 2 summarizes the extent of HBS implementation for all transit agencies that completed the survey. Five agencies reported full implementation of HBS, four reported transitioning to or testing HBS, and seven had not implemented HBS at all. Among the nine transit agencies that reported a full or partial implementation of HBS, all respondents reported using APC, CAD, EFP, SS, and smartphone apps for real-time arrival information, as shown in Table 3. Each ITS technology was used by at least seven agencies. Ineligible Respondents Among the seven transit agencies that have not implemented HBS at all, six are considering an implementation but have not yet conducted a feasibility study or pilot test. One agency had no plans to implement. Headway-Based Service The nine transit agencies that reported having fully or partially implemented HBS reported a total of 15 routes with HBS. Table 4 summarizes these nine agencies by the number of headway- based routes and weekday peak/off-peak hour target headways. A majority of these agencies reported weekday peak-hour target headways of 6 to 10 minutes and weekday off-peak-hour target headways of 11 to 15 minutes. Most surveyed agencies were recent implementers of HBS, with a median of 2 years’ experience. The most recent implementation was less than 1 year ago, and the longest running had been in service for 11 years. Figure 4 shows the reasons for implementing HBS. Other responses included “more reliable calculation of customer experience regarding on-time performance” and “space buses evenly to avoid pass-ups.” Survey respondents only operate HBS on a small subset of routes in their network. Table 5 shows the selection criteria used. The most common responses were “route type,” such as a BRT C H A P T E R 3

Survey Results 21   Number of Headway-Based Routes Weekday Peak-Hour Target Headway (Minutes) Weekday Off-Peak-Hour Target Headway (Minutes) 0–5 6–10 11–15 6–10 11–15 15+ 1 1 3 0 1 2 1 2 0 4 1 0 3 2 3 0 1 1 0 1 0 Table 4. Number of headway-based routes by peak/off-peak hour targets. Agency Name Location Extent of HBSImplementation Capital Metro Austin, TX Fully implemented Montgomery County, Maryland Rockville, MD Fully implemented Snohomish County Public Transportation Benefit Area Corporation Everett, WA Fully implemented City and County of Honolulu Department of Transportation Services Honolulu, HI Fully implemented Washington Metropolitan Area Transit Authority Washington, D.C. Fully implemented Alameda-Contra Costa Transit District Oakland, CA In transition or testing phase Metropolitan Transit Authority of Harris County, Texas Houston, TX In transition or testing phase LA Metro Los Angeles, CA In transition or testing phase Southeastern Pennsylvania Transportation Authority Philadelphia, PA In transition or testing phase Central Florida Regional Transportation Authority Orlando, FL Not implemented at all Capital District Transportation Authority Albany, NY Not implemented at all Lane Transit District Eugene, OR Not implemented at all York Region Transit Richmond Hill, Ontario, Canada Not implemented at all San Diego Metropolitan Transit System San Diego, CA Not implemented at all CTA Chicago, IL Not implemented at all Denver Regional Transportation District Denver, CO Not implemented at all Table 2. Extent of headway-based service implementation. Extent of HBS Implementation ITS Technology Used APC AVL CAD EFP PIS SS Apps TSP Fully implemented 5 5 5 5 5 5 5 4 In transition or testing phase 4 3 4 4 3 4 4 3 Total 9 8 9 9 8 9 9 7 Table 3. ITS technologies used to help manage headway-based service. or rapid service, followed by “bus frequency/headway.” Other criteria included “a 15-minute or greater frequency on a single route alignment (no branching).” Table 6 presents a high-level overview of how transit agencies said they were using various ITS components to support HBS. An X identifies how a majority of transit organizations use ITS to support HBS. TSP was the only ITS technology that was not used by a majority of transit organizations for a specific purpose. In this case, an O identifies how a plurality of transit organizations uses TSP to support HBS. The tables in Appendix B provide more detail about how transit agencies use each of these ITS technologies to support HBS. As Table 7 shows, the majority of survey respondents do not send or receive ITS data with external systems. Of those that do, the most common data link was with a municipal (city)

22 Intelligent Transportation Systems in Headway-Based Bus Service 0 1 2 3 4 5 6 Other Response to increase in service frequency Address operational problems Simplify passenger information Count Figure 4. Reasons headway-based service adopted. Bus Route Selection Criteria Count Route type 6 Bus frequency/headway 5 Street or bus corridor type 3 Observed service problems 2 Other 2 Table 5. Reason route selected for headway-based service. How ITS Supports HBS ITS Technology UsedAPC AVL CAD EFP PIS SS App TSP Collect passenger counts X X Track vehicle loading (NA) Track bus location X Provide passenger information X X X Estimate headways X Record bus trajectories X Respond to incidents X Monitor headways X Implement control strategies X Save data for later analysis and/or enforcement X Allow buses behind schedule to request priority O X = use ITS to support HBS; O = use TSP to support HBS; NA = not applicable. Table 6. How ITS supports headway-based service. Systems that Transfer/Receive ITS Data Count City 3 County 2 Department of transportation 2 Third-party provider 2 Metropolitan planning organization 1 No data communicated 6 Table 7. Systems that transmit/ receive ITS data.

Survey Results 23   system. Types of data received from external systems included signal timing, bus schedules, granted TSP requests, and related traffic information. Holding at control points was the most often mentioned strategy when transit agencies were asked about operational strategies used to maintain headways or correct bunching and gapping, with stop skipping the next most often mentioned strategy as shown in Table 8. The four transit agencies that reported using short turns as an operational strategy to maintain headways were asked what rule is used to determine when to execute a short turn. Two agencies stated ridership and vehicle spacing are reviewed on a case-by-case basis, with no uniform rule applied across all situations. One survey respondent said that they use short turns to get a bus back to its place in the schedule or to take the place of another bus (filling in a gap in service). The three transit agencies that reported using stage vehicle insertion as an operational strategy to maintain headways were asked what rule is used for adding a vehicle to the HBS, either staged or from the garage. One agency stated buses are strategically staged at locations throughout the district and inserted in place of turning back or to provide service relief to routes that experience delay or heavy ridership. Another agency stated ridership and vehicle spacing are reviewed on a case-by-case basis, with no uniform rule applied across all situations. Those agencies were also asked how many vehicles were typically staged to be used as inserts. One agency reported staging a single vehicle. Another agency stated it typically staged buses at various known problem sections around the city. The agency went on to state it may have as many as 25 vehicles staged throughout the region. Transit agencies with HBS that used at least one of the ITS technologies listed in Table 3 to manage this service were next asked about infrastructure used to support headway adherence. Dedicated bus lanes were the most often mentioned infrastructure, with queue jumps, off-board fare payment, and all-door boarding the next most (and equally) popular, as shown in Table 9. Operational Strategy Used to Maintain Headways Count Holding at control points 9 Stop skipping 6 Speed guidance 5 Boarding limits 4 Short turns 4 Stage vehicle insertion 3 Table 8. Operational strategy used to maintain headways. Headway Adherence Support Infrastructure Count Dedicated bus lanes 5 Queue jumps 3 Off-board fare payment 3 All-door boarding 3 Bus stop bulbs 1 None of these aAn extension of the sidewalk allowing buses to stop and serve passengers without pulling out of their travel lane. Bus stop bulbs can improve travel time reliability for buses by removing the need to merge back into traffic when exiting the bus stop. 2 Other 3 a Table 9. Headway adherence support infrastructure.

24 Intelligent Transportation Systems in Headway-Based Bus Service Two agencies reported using none of the listed infrastructure types. Other responses suggested that some transit agencies are using modified farebox policies, while some agencies are still experimenting with various types of infrastructure. Of the nine agencies with HBS that used at least one of the ITS technologies listed in Table 3 to manage this service, • Eight agencies confirmed they had dispatchers (or supervisors in the field) dedicated to monitoring HBS for at least a portion of the day, but that these dedicated dispatchers may also monitor schedule-based services at the same time. • Five agencies indicated that their agency has some sort of dispatcher training or standard operating procedures specific to HBS. Performance Monitoring and Benefits When the nine agencies with HBS were asked how their agency quantitatively measured the performance of HBS, eight agencies identified headway adherence, which was, by far, the most popular mechanism, as shown in Table 10. Using a scale of 1 (made significantly worse) to 5 (made significantly better), these nine agencies were asked to evaluate the impact of launching a HBS on several factors. For analytical purposes, the scale was transformed as outlined as follows, which assigned the same neutral value to no impact, unknown, and missing; this recoding was deemed acceptable because of the original scale’s neutral midpoint: • Made significantly worse: −2. • Made somewhat worse: −1. • No impact, unknown, or missing: 0. • Made somewhat better: 1. • Made significantly better: 2. Table 11 shows the average rating on the transformed scale for each factor. The most bene ficial factors were headway adherence, bus travel time variability, and passenger wait time variability. The least beneficial factor was ridership, though its rating is still a net positive. No agency evaluated any single factor as “made somewhat worse” or “made significantly worse.” Using a scale of 1 (not useful at all) to 3 (very useful), these nine transit agencies were asked to evaluate the usefulness of several ITS technologies in operating HBS. Since not every agency had every technology, “N/A” and “missing” responses were removed, and an average rating was calculated from the remaining responses. Table 12 shows the average rating and the number of responses received for each technology. The most useful technologies were AVL and CAD. The least useful technology was SS. Mechanism Count Headway adherence 8 Bus travel times 3 Number of trips 3 Passenger satisfaction 3 Headway variability 2 Bus travel time variability 2 Passenger wait time 2 Other 1 Table 10. Mechanism to quantitatively measure HBS performance.

Survey Results 25   Using a scale of 1 (not effective at all) to 3 (very effective), these nine transit agencies were asked to evaluate the effectiveness of the several operational strategies at managing headways. Since not every agency used every strategy, “N/A” and “missing” responses were removed, and an average rating was calculated from the remaining responses. Table 13 shows the average rating and number of responses recorded for each strategy. The most effective strategy was short turn. The least effective strategy was stop skipping. Factor Average Rating Headway adherence 0.78 Bus travel time variability 0.78 Passenger wait time variability 0.78 Passenger wait time 0.67 Headway variability 0.67 Bus travel times 0.56 Passenger satisfaction 0.44 Ridership 0.33 Mean 0.63 Table 11. Evaluation of headway- based service on various transit system factors. ITS Technology Average Rating Responses AVL 3.00 8 CAD 3.00 8 EFP 2.88 8 PIS 2.75 8 TSP 2.71 7 APC 2.50 8 Smartphone app 2.38 8 SS 1.88 8 Mean 2.64 Table 12. Evaluation of ITS technologies in HBS operation. Operational Strategy Average Rating Responses Short turn 2.67 6 Speed guidance 2.57 7 Holding at control points 2.50 8 Stage vehicle insertion 2.50 4 Boarding limits 2.33 6 Stop skipping 1.83 6 Mean 2.40 Table 13. Evaluation of operational strategies for managing headways.

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Intelligent transportation systems and, in particular, computer-aided dispatch and automatic vehicle location (CAD/AVL), have become quasi-universal in urban bus operations and support a variety of functions.

The TRB Transit Cooperative Research Program's TCRP Synthesis 155: Intelligent Transportation Systems in Headway-Based Bus Service synthesizes the current state of the practice of headway-based service operations and focuses on the proactive use of intelligent transportation systems technologies to optimize these services.

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