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1 Summary Transit ridership across the United States has declined for six straight years. Bus ridership, which has declined more than other transit services, is now at the lowest point since 1965. Rail ridership, with the exception of commuter rail, has also declined, and commuter rail ridership has recently leveled off. Research Objective and Approach The objectives of this research were to (1) produce 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 and (2) explore and present strategies that transit agencies are considering and using for all transit modes in response to changes in ridership. The research approach included a literature review, transit ridership analysis, and case studies. Ridership Analysis by Cluster The research on system-wide changes in transit ridership presented in this report was organized around two sets of clusters that grouped transit agencies according to similar operating environments and service characteristics. As shown in Table 1, one cluster analysis was for regions with transit services in mixed traffic (typically bus-based services), and the other cluster analysis was of regions with transit services in a dedicated right-of-way (typically rail-based service). TableÂ 1:Â TransitÂ AgencyÂ ClustersÂ Mixed Traffic Clusters Dedicated Right-of-Way Clusters Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Mid-sized, transit-oriented Mid-sized, auto-oriented Sprawling small towns Sprawling metropolis Dense metropolis Cluster A Cluster B Cluster C Cluster D Cluster E Los Angeles Dense metropolis Mid-sized, dense Mid-sized, dense, auto-oriented Sprawling metropolis In the analysis produced for this report, we have used the clustered regions to produce a current snapshot of public transit ridership trends. For each cluster, a trend analysis was performed to examine the relationship between transit ridership and the three major factors influencing transit ridership: population, transit dependent population (i.e., zero-vehicle households) and transit service levels (i.e., transit vehicle revenue miles). Historically, transit ridership has increased with increases in each of these factors.
2 In each case, the relationship between transit ridership and each of these three factors is first evaluated using only 2012 data to understand the steady-state effects each factor has on transit ridership after decades of interaction. Then, the percentage change in transit ridership is compared to the percentage change in each of the three factors between 2012 and 2016 to understand their relationship in the recent past. The results are shown in Table 2. Additional key points from the transit ridership change analysis are summarized below: ï· Although not uniformly true, in most regions, population has increased, therefore transit ridership per capita has been falling at an even faster rate than total transit ridership. Population is a strong predictor for bus ridership historically, but mixed traffic (generally bus) ridership change seems unaffected by the increases in population. Population is a more moderate predictor for dedicated right-of-way (mostly rail) ridership historically and also population change explains some of the recent rail ridership changes. ï· Transit-dependent population is not a good predictor of ridership or ridership change. ï· The amount of transit service provided is an important lever available for transit agencies to affect transit ridership. The relationship between transit ridership and transit service levels is strong. Especially in mid-sized MSAs, transit service levels explain almost all of the variation in transit ridership. However, in looking at recent changes in transit service, in the larger metro areas, more bus service does not equal more bus riders. The change in transit ridership is much more closely associated with recent change in transit service levels for dedicated right-of-way modes than for mixed traffic modes. ï· Each marginal vehicle revenue mile is associated with twice the transit ridership in mid- sized transit-oriented regions, such as those in the âRust Beltâ than in similar mid-size car- oriented regions in the âSun Beltâ. Similarly, the relationship between transit ridership and transit service levels is three times greater for transit-oriented metro areas than for car- oriented metro areas. In other words, increasing transit service in denser transit- oriented regions (both mid-size and large metros) will increase transit ridership much more than car-oriented regions. ï· Small to mid-sized regions that didnât increase transit service levels between 2012 and 2016 should expect 8-10% loss in transit ridership. The y-axis intercept of the trend lines in transit service change versus transit ridership change figure is the amount of ridership change that should be expected if transit service levels had not changed (x=0). Although there is a definite relationship between the change in transit ridership and the change in transit service levels, there is some other effect at play that is driving transit ridership down across clusters. Only if transit service was substantially increased would transit ridership go up. If service levels remained the same, in most regions, transit ridership would have decreased.
3 TableÂ 2:Â AnalysisÂ ofÂ factorsÂ impactingÂ transitÂ ridershipÂ andÂ changeÂ inÂ transitÂ ridershipÂ Population Transit-dependent Population Transit Service Levels Mixed Traffic ROW (Intra-city bus, commuter bus, bus rapid transit, and streetcar service) 2012 Strong relationship for population and ridership in every cluster except sprawling metros (Cluster 4). Very little relationship between zero-vehicle households and transit ridership. Strong relationship between transit ridership and transit service levels, especially in mid- sized MSAs. Change from 2012-2016 No relationship linking cities that had population gains to increases in transit ridership. Change in transit ridership and change in zero vehicle households are only linked in the largest metros. Change in service also more strongly linked to change in ridership in mid-sized MSAs, but non- existent in larger metros. Dedicated ROW (Heavy rail, light rail, monorail, and hybrid rail) 2012 Moderate relationship for population and transit ridership. Minimal relationship between zero-vehicle households and transit ridership. Strong relationship between transit ridership and transit service levels. Change from 2012-2016 Also, moderate relationship for change in population and change in transit ridership. No relationship between change in zero-vehicle households and change in ridership. Moderate relationship between change in transit service and change in transit ridership.
4 Strategies to Improve Transit Ridership Transit agencies throughout the U.S. have initiated or are developing strategies to improve customer service and increase transit ridership. This research project identified many of these strategies through the literature and news article review. Strategies transit agencies are undertaking include: ï· Increasing transit service levels, by restructuring bus networks, and service expansion through adding new modes, such as light or heavy rail. Transit agencies are also adding dedicated right-of-way by increasing the use of bus rapid transit. ï· Adding new mobility options. An emerging area includes partnerships with Transportation Network Companies (TNCs) and bike, scooter and car sharing companies, either to subsidize trips or through data partnerships. Similarly, some transit agencies are adding demand response and flex routes that function like the TNC services, but are provided by the transit agency in the form of microtransit pilots. ï· Improving technology and customer amenities. Finally, technology improvements, including new fare media and better fare media integration, as well as real-time information are improving customer service. Many of these strategies, those increasing in adoption, have not been widely studied as to their impacts on transit ridership. Although some anecdotal evidence was provided by the case studies, far more research is needed to understand the impacts of these strategies on transit ridership. Case Studies Ten case studies were undertaken to better understand individual strategies transit agencies are using to mitigate ridership losses and increase ridership overall. Transit agencies were asked about their strategies, ridership over the past several years, and speed and reliability metrics. The strategies used by the case study transit agencies and the resulting ridership changes are summarized in Table 3. Some key results from the case studies include: ï· Nearly every transit agency investigated in the case studies had ridership increases through 2015 followed by steady decreases in ridership. The exceptions to this are Houston, TX, Portland, ME, and Seattle, WA, which all saw steady or increasing ridership, but also increased service substantially. In all other cases, among the transit agencies where ridership declined, the amount of service provided has remained relatively similar over this time or has only been slightly increased. ï· In every transit agency reviewed, average speeds have decreased or have remained the same, indicating that more vehicles are frequently needed to offer the same or degraded service. Some transit agencies have fought hard to keep average speeds up using strategic improvements such as signal priority or improvements to boarding.