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.
1 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 at least 1973. 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 (bus and rail) ridership trends in urban and suburban areas in the U.S., focusing on what has changed in the past several years, and (2) explore and present strategies that transit agen- cies 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 systemwide changes in transit ridership presented in this report was organized around two sets of clusters that grouped transit agencies according to simi- lar 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 (ROW) (typically rail-based service). 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. 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 rider- ship 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 include the following: â¢ Although not uniformly true, in most regions, population has increased; thus transit ridership per capita has been falling at an even faster rate than total transit ridership. S U M M A R Y Analysis of Recent Public Transit Ridership Trends
2 Analysis of Recent Public Transit Ridership Trends Change from 2012 to 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. 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 to 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 nonexistent 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. Table 2. Analysis of factors impacting transit ridership and change in transit ridership. Mixed Traffic Clusters Dedicated Right-of-Way (ROW) Clusters Cluster 1 Cluster 2 Cluster 3 Mid-sized, transit-oriented Mid-sized, auto-oriented Sprawling small towns Cluster A Cluster B Cluster C Los Angeles Dense metropolis Mid-sized, dense Cluster 4 Cluster 5 Sprawling metropolis Dense metropolis Cluster D Cluster E Mid-sized, dense, auto-oriented Sprawling metropolis Table 1. Transit agency clusters.
Summary 3 Population has historically been a strong predictor for bus ridership, but mixed traffic (generally bus) ridership change seems unaffected by the increases in population. Population is a more moderate predictor for dedicated ROW (mostly rail) ridership historically, and population change explains some of the recent rail ridership changes. â¢ Transit-dependent population is not a good predictor of ridership or ridership change. â¢ The amount of transit service provided is an important lever available for transit agen- cies to affect transit ridership. The relationship between transit ridership and transit service levels is strong. Especially in mid-sized metropolitan statistical areas (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 ROW modes than for mixed traffic modes. â¢ Each marginal vehicle revenue mile is associated with twice the transit ridership in mid- sized transit-oriented regions, such as those in the Rust Belt than in similar midsize car- oriented regions in the Sun Belt. 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 midsize and large metros) will increase transit ridership much more than car-oriented regions. â¢ Small to mid-sized regions that did not increase transit service levels between 2012 and 2016 should expect 8â10% 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 rider- ship would have decreased. 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 â¢ 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 ROW by increasing the use of bus rapid transit. â¢ Adding new mobility options. An emerging area includes partnerships with transporta- tion 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. â¢ Improving technology and customer amenities. 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
4 Analysis of Recent Public Transit Ridership Trends 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 agen- cies 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 reli- ability metrics. The strategies used by the case study transit agencies and the resulting rider- ship changes are summarized in Table 3. Some key results from the case studies include the following: â¢ 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 rider- ship 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 slightly increased. â¢ 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. â¢ Generally, on-time performance has been improving, although it is not causing transit ridership to increase. If anything, the trend appears that on-time performance is easier to maintain as ridership has decreased. â¢ Rail ridership declines have occurred later than bus ridership declines, but a similar pattern exists. Only with substantial increases in transit service have there been sub- stantial increases in ridership. Commuter rail seems to be faring better. Whatever is impacting bus transit ridership across the country does not have the same impact on the dedicated ROW longer-distance commuter rail services. Table 3. Case study results. Agency Strategies Results Connect Transit BloomingtonâNormal, IL â¢ Network redesign â¢ Increased frequency â¢ Real-time information Ridership up until 2015, then down through 2017, slowly increasing again. Greater Portland Metro Portland, ME â¢ Speed and reliability improvements â¢ High school and university partnerships â¢ Real-time information â¢ Express routes Ridership up until 2017 and then steady. Average speed also increasing. IndyGo Indianapolis, IN â¢ Expanded frequency and hours â¢ Downtown Transit Center Ridership down since 2015. Average speeds down, but on- time performance has improved.
Summary 5 Agency Strategies Results King County Metro Seattle, WA â¢ Bus Rapid Transit â¢ Improved fare payment â¢ New streetcar Bus ridership up until 2017 and then steady. Average speeds down. Rail ridership up steadily since 2016 with new service. Maryland Transit Administration Baltimore, MD â¢ Network redesign Bus ridership up until 2015 and then down since then. Light and heavy rail ridership down since 2013. Commuter rail ridership up until 2015, then steady. Massachusetts Bay Transportation Authority Boston, MA â¢ Added service â¢ Bus Rapid Transit â¢ Speed and reliability improvements Bus ridership up until 2015 and down since then, but recently steady. Heavy and light rail ridership steady until 2017, then down. Commuter rail ridership down in 2015, then steady. Metro Transit Minneapolis, MN â¢ Bus Rapid Transit â¢ New light rail line â¢ New commuter rail station Bus ridership down since 2015. Light rail ridership and service hours up after new line in 2014, but steady since 2016. Commuter rail ridership up in 2014, then back down and steady, until up in 2018. Metro Transit Authority of Harris County Houston, TX â¢ Network redesign â¢ Real-time information â¢ Improved fare payment Bus ridership unchanged. Light rail ridership and service hours up after new lines in 2013 and 2015, but steady since 2016. Pinellas Suncoast Transit Authority Pinellas County, FL â¢ TNC partnership Bus ridership down since 2016. Demand response ridership (TNC trips) up. Spokane Transit Authority Spokane, WA â¢ Real-time information â¢ Increased service and frequency Ridership up until 2015, then down through 2017, slowly increasing again with increased frequency. Table 3. (Continued).