National Academies Press: OpenBook

Analysis of Recent Public Transit Ridership Trends (2020)

Chapter: Chapter 6 - Conclusions and Next Steps

« Previous: Chapter 5 - Case Studies
Page 72
Suggested Citation:"Chapter 6 - Conclusions and Next Steps." National Academies of Sciences, Engineering, and Medicine. 2020. Analysis of Recent Public Transit Ridership Trends. Washington, DC: The National Academies Press. doi: 10.17226/25635.
×
Page 72
Page 73
Suggested Citation:"Chapter 6 - Conclusions and Next Steps." National Academies of Sciences, Engineering, and Medicine. 2020. Analysis of Recent Public Transit Ridership Trends. Washington, DC: The National Academies Press. doi: 10.17226/25635.
×
Page 73

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

67 Figure 40: MBTA Commuter Rail Systemwide Trends  Fixed route bus ridership data for Figure 41 and Figure 42 is weekday automated passenger counter (APC) data averaged monthly over the fall period (September, October, November, and December) provided by the transit agency. Frequency data were obtained from archived GTFS (General Transit Feed Specification). MBTA defined 15 key bus routes with high ridership, service frequency, and span of service hours. These key bus routes are displayed in Figure 41 and Figure 42. Key routes with increases in service frequencies between 2014 and 2017 did not see increases in ridership as seen in Figure 41. Figure 41: MBTA Bus Frequency and Ridership Trends in 2014 and 2017 

68 MBTA defines on-time performance for frequent bus service as a departure between 0 minutes before and 3 minutes after its scheduled departure. Infrequent bus service is defined as on-time if it arrives 1 minute ahead to 6 minutes behind the posted schedule. The on-time performance metric for each route is calculated as a percentage of the number of on-time arrivals divided by the total number of arrivals at time stops. The on-time performance metric averages weekday peak and off- peak service during the fall period is displayed per route in Figure 42. On-time performance has increased on all fixed bus key routes. Figure 42: On‐time Performance and Ridership Trends in 2014 and 2017  Regarding impacts from the bus lanes, in MBTA’s own analysis, the Washington Street AM peak bus lane in Roslindale neighborhood has seen an increase of 4% in boardings along the corridor comparing Fall 2017-18 to Fall 2018-19. However, this analysis does not address that some riders may be coming from other routes rather than being new to MBTA service and some may be due to growth in the neighborhood population rather than the bus lane specifically. MBTA continues to conduct analysis of their ridership impacts from these projects and across their system. Future Plans to Encourage Ridership MBTA continues to monitor ridership and system performance to encourage transit ridership. Utilizing metrics and analysis, MBTA is planning a number of projects including station

Next: Bibliography »
Analysis of Recent Public Transit Ridership Trends Get This Book
×
MyNAP members save 10% online.
Login or Register to save!
Download Free PDF

Transit ridership is down across all modes except commuter rail and demand response. Bus ridership is down the most in mid-size cities (populations of 200,000 – 500,000), and, after six years of consecutive decline, it is at its lowest point overall since the 1970s.

The TRB Transit Cooperative Research Program's TCRP Research Report 209: Analysis of Recent Public Transit Ridership Trends presents a current snapshot of public transit ridership trends in the U.S. on bus and rail services in urban and suburban areas, focusing on what has changed in the past several years. It also explores and presents strategies that transit agencies are considering and using for all transit modes in response to changes in ridership.

Ten case studies are included to better understand individual strategies transit agencies are using to mitigate ridership losses and increase ridership overall. Seven of the 10 transit agencies investigated in the case studies followed the trend, with ridership increases between 2012 and 2015 followed by steady decreases in ridership. Generally, on-time performance has been improving, although it is not causing transit ridership to increase.

  1. ×

    Welcome to OpenBook!

    You're looking at OpenBook, NAP.edu's online reading room since 1999. Based on feedback from you, our users, we've made some improvements that make it easier than ever to read thousands of publications on our website.

    Do you want to take a quick tour of the OpenBook's features?

    No Thanks Take a Tour »
  2. ×

    Show this book's table of contents, where you can jump to any chapter by name.

    « Back Next »
  3. ×

    ...or use these buttons to go back to the previous chapter or skip to the next one.

    « Back Next »
  4. ×

    Jump up to the previous page or down to the next one. Also, you can type in a page number and press Enter to go directly to that page in the book.

    « Back Next »
  5. ×

    To search the entire text of this book, type in your search term here and press Enter.

    « Back Next »
  6. ×

    Share a link to this book page on your preferred social network or via email.

    « Back Next »
  7. ×

    View our suggested citation for this chapter.

    « Back Next »
  8. ×

    Ready to take your reading offline? Click here to buy this book in print or download it as a free PDF, if available.

    « Back Next »
Stay Connected!