National Academies Press: OpenBook

Analysis of Recent Public Transit Ridership Trends (2020)

Chapter:Chapter 6 - Conclusions and Next Steps

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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 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.

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72 In the United States, transit ridership overall has declined for six straight years. Bus ridership is at the lowest point since at least 1973, and rail ridership has decreased over the past few years, as well. There are many possible factors for this decline in ridership. A recent APTA report summed up many of the factors in four main areas: erosion of time competitiveness, reduced affinity, erosion of cost competitiveness, and external factors (APTA, 2018b). • Erosion of time competitiveness relates to increased congestion in cities from densification, delivery services, and TNCs, causing decreasing speed on shared ROW transit services such as traditional bus. Due to these decreasing speeds, additional service hours are needed just to maintain existing headways. • Reduced customer affinity and loyalty stems from changing populations that are less apt to purchase a monthly pass because they telework or use multiple modes. • Cost competitiveness relates to the lower cost of auto ownership and inexpensive TNC fares. • External factors include parking availability and movement of major generators outside of dense areas. The recent decline in transit ridership is particularly worrisome because traditional factors of effecting transit ridership do not seem to be involved. According to data from the National Transit Database, transit agencies have increased bus service (vehicle revenue miles) by 5% between 2012 and 2016. Although our analysis found that amount of service provided was a strong predictor of both bus and rail transit ridership using 2012 data, the change in service levels was only a predictor of change in ridership in smaller cities or for dedicated ROW. In fact, we found that transit agencies had to increase service by 8–10% from 2012 to 2016 to expect unlinked passenger trips to remain unchanged. Meanwhile, urban population in the United States is at its highest point in recorded history (Ratcliffe, 2012), and urban core areas have grown in population every year since 2006 (Frey, 2018). Although population is still a strong predictor of the level of transit ridership, espe- cially for bus ridership in denser cities, our analysis found that population change and ridership change were entirely uncorrelated for bus and only somewhat correlated for rail. The health of the economy should also be encouraging people to make more transit trips. In 2017, unemploy- ment levels in the United States were at their lowest level since the recession in 2009. A potential contributing factor to the decreasing transit ridership is the economic displace- ment of low-income earners from dense urban-centers to the suburbs (Florida, 2017). While cities are becoming denser, their populations have higher-incomes and more cars. Studies in Portland, OR, and Southern California have verified that low-income migration may be impact- ing transit ridership. In our analysis, the 2012 proportion of zero-vehicle households and transit ridership are not strongly linked, but the 2012 to 2016 change in zero-vehicle households and transit ridership are linked in the largest cities. C H A P T E R 6 Conclusions and Next Steps

Conclusions and Next Steps 73 The decreases in transit ridership found in the last four years were not only in the largest cities but across the board. Nearly every transit agency investigated in the case studies had ridership increases through 2015, followed by steady decreases in ridership. Giving credence to the APTA time competitiveness factor, in every case study transit agency, average speeds are down or have remained the same. Commuter rail seems to be faring better across the country, and the transit agencies among the case studies are no different. Whatever is impacting bus transit ridership across the country does not have the same impact on the dedicated ROW longer distance commuter rail services. In an attempt to turn the declining transit ridership trend around, transit agencies have imple- mented new strategies. Transit agencies such as Houston and Baltimore are adding service and redesigning their networks to increase frequencies on their core routes and attract new riders. Others such as Portland, ME, and Spokane, WA, are adding service to attract certain popula- tions. New pricing schemes and fare technologies are helping to incentivize riders and reduce the friction in transit fare purchasing. Transit agencies are implementing microtransit pilots to provide a similar experience to TNCs or are partnering with TNCs to subsidize rides. Finally, transit agencies are using improvements to speed and reliability to improve service and ridership strategically, especially through more dedicated ROW that prioritizes transit over general traffic. Future Research The question that remains is how much these strategies can help mitigate and reverse the declines in transit ridership and how transit agencies can most efficiently implement these changes. Although there is a growing body of research on these factors, we still lack a compre- hensive understanding of the extent to which various factors impact transit ridership, and many of the strategies transit agencies are using to mitigate or reverse trends are not well understood from a ridership impact perspective. Population trends segmented by multiple factors such as age group, race and ethnicity, and income levels should be explored in greater detail to explain the impact of baby-boomer retirement, millennial transportation patterns, gentrification, and other similar migrations within a city. Further research is needed, especially at a disaggregate level that looks not at ridership on a city by city basis but on a route by route and zone by zone basis using fare card and passenger counter data to understand where transit ridership is decreasing within a city and what external factors are impacting those decreases. Further research should assess not just rider- ship change on a yearly or even monthly basis, but should segment ridership into types of trips (long distance, short distance, commuter, off peak), as sometimes ridership increases in one area can temporarily mask declines in another. Assessing individual trip behavior can also be a key to understanding how ridership is changing. Additional work by TCRP is being conducted through TCRP A-43, “Recent Decline in Public Transportation Ridership: Analysis, Causes, Responses”; and TCRP H-56, “Redesigning Public Transportation Networks for a New Mobility Future.” Both of these projects will conduct deeper dives into understanding the ridership question in a new mobility future.

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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.

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