Analysis of Recent Public Transit Ridership Trends (2020)

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10 Transit agencies in the United States operate in a wide variety of environments, from small towns to mega regions, where decades of urban development have shaped the way people travel. This context affects not only the contributors to changing transit ridership, but also which strat- egies may be effective at offsetting ridership declines. While the overall ridership trend is point- ing downward, it is important to identify sub-trends in order to grasp the full implications. Identifying the characteristics associated with transit ridership decline is also necessary to effec- tively target its root causes. Discerning the sub-trends is particularly relevant because the largest transit agencies account for a disproportionate share of ridership; the New York MTA alone contributed 33% of 2015 unlinked transit passenger trips in the U.S. The ridership decline could be attributed to a few large transit agencies, for example due to extended rail closure; or it could be attributed to many small ones, for example due to urban migration; or it could be attributed to both. Furthermore, any analysis of averages would skew towards the largest regions and overlook ridership trends in smaller ones. Organizing transit agencies into groups of peers is necessary to compare the evolution of transit ridership over time. With this knowledge, the research presented in this report was organized around two sets of clusters that group transit agencies according to similar operating environments and service characteristics. Using the clusters, national ridership trends were identified and graphed along with changes in population, transit vehicle revenue miles, and zero-vehicle households. Then, ten case study transit agencies were selected across the clusters to look at route-level ridership change within the transit agency. Clustering The first step of this analysis of ridership trends is to classify transit agencies with similar operating environments and service characteristics. A full description of the methodology used is described in a Transportation Research Record paper titled “Comparing Transit Agency Peer Groups Using Cluster Analysis” (Ederer et al., 2019). Transit regions were clustered into groups of peers on the basis of metropolitan area population, percentage of population living in a dense area, percentage of zero-vehicle households, and transit operating expenses. Two cluster analyses were performed: one for transit services in mixed traffic and one for services in a dedicated ROW. The mixed traffic and dedicated ROW mode categories were sepa- rated based on National Transit Database data. • Mixed traffic regions included all metro areas operating intra-city bus, commuter bus, BRT, and streetcar service. C H A P T E R 2 Research Approach

Research Approach 11 • Dedicated ROW modes included heavy rail, light rail, monorail, and hybrid rail. Dedi- cated ROW services only included systems with 1 million or more unlinked passenger trips per year. Transit agencies that operate mixed and dedicated ROW service were included in both clusters. Metrics attributed to different modes were split according to mode for each clustering. This method captures the differences in operation and funding logistics that may be present for dif- ferent modes within the same transit agency and region. With the understanding that many transit agencies operate in the same city, and that riders have little discretion for the specific transit agency operating a service, we found it useful to group regions rather than transit agencies. Transit providers within a region often compete for the same riders or connect groups of riders together, so pooling all of the transit service in a region provides a much more useful glimpse into particular ridership trends in a city than an agency-by-agency analysis. We clustered regions based on their core-based statistical area, often known as metro- politan or micropolitan statistical areas. This core-based statistical area was chosen as it has the most data availability for any regional metric from the U.S. Census. American Community Survey (ACS) 5-year estimates were used for the years 2012 and 2016, as well as transit data from the National Transit Database supplemented with data from the American Public Transporta- tion Association (APTA). The availability of timely data was a limitation of the study, as 2016 data was the most recent available at the time of analysis. Downward trends in transit ridership have continued into 2017 and 2018 with some cases being even more substantial than what is shown in this report. Clusters—Mixed Traffic Modes The resulting clusters are described below. Figure 4 shows a map of mixed traffic regions color-coded by cluster. In all cluster solutions, the New York City metropolitan area was an outlier. It was not included in this analysis. • Cluster 1: Mid-sized, transit-oriented. This features older industrial cities that are typi- cally in the Northeast and Midwest that have declined in population in the past several decades. These areas have a relatively high number of zero-vehicle households and are typically small to midsize metro areas. Cities include Albany, Baltimore, Pittsburgh, and Cleveland. • Cluster 2: Mid-sized, auto-oriented. This features primarily smaller, recently developed cities in the Midwest and South with low percentages of people living in zero-vehicle households. Cities include Indianapolis, Kansas City, Charlotte, and Nashville. • Cluster 3: Sprawling small towns. This consists of the smallest cities operating fixed-route transit service and includes a disproportionate number of “college towns.” The metro areas in this cluster are the least dense, least populated, and spend the least on transit of the tran- sit agencies included in this analysis. Cities include Lansing, Burlington, Blacksburg, and Knoxville. • Cluster 4: Sprawling metropolis. The cities in this cluster are sprawling, large cities that have a low percentage of zero-vehicle households. Operating expenditures in this cluster reflect the large population of these areas. Cities include Atlanta, Houston, Denver, and Phoenix. • Cluster 5: Dense metropolis. This consists of the largest metro areas in the United States. Metro areas in this cluster are very dense and spend substantially more on bus operations than regions in other clusters. Example cities include Boston, Philadelphia, Chicago, Seattle, and Miami.

Figure 4. Map of mixed traffic transit regions by cluster.

Research Approach 13 Clusters—Dedicated Right-of-Way Modes The resulting dedicated ROW clusters are described below. Figure 5 delineates the clus- ters for metropolitan areas operating dedicated ROW services with at least 1 million trips per year. • Cluster A: Los Angeles. The Los Angeles metropolitan area is an outlier in this grouping. It is unusually large with a higher percentage of people in dense areas but with very low investment in dedicated ROW service. • Cluster B: Dense metropolis. This includes Chicago, Boston, Philadelphia, San Francisco, and Washington D.C. These are large metro areas with extensive transit systems and large commuter rail networks. • Cluster C: Mid-sized, dense. This consists of cities that are relatively small, compact, and with a high number of zero-vehicle households. This includes former industrial hubs in Baltimore, Buffalo, Cleveland, and Pittsburgh. • Cluster D: Mid-sized, dense, auto-oriented. This consists of medium-sized metro areas that are mainly in the western areas of the country, such as San Jose, Portland, Seattle, Phoenix, Sacramento, Denver, and San Diego as well as Miami. These cities have low per- centages of zero-vehicle households but a high proportion of population living in dense census tracts. • Cluster E: Sprawling metropolis. This consists of sprawling large metro areas with relatively few dense census tracts, many of which are located in the southern (Atlanta, Dallas, Houston, Charlotte) and western (Salt Lake City, Minneapolis, St. Louis) regions of the U.S. Figure 5 presents the clusters in the form of a dendogram, in which the regions most closely related are shown as connected by a line. Cluster A (Los Angeles) is therefore more closely related to Cluster B (dense metropolis) than to the other clusters. Similarly, Cluster D (mid-sized auto-oriented) and Cluster E (sprawling metropolis) are more closely related to each other than the other clusters, and so on. EA B C D Figure 5. Dendogram of dedicated ROW clusters.

14 Analysis of Recent Public Transit Ridership Trends Ridership Trends It is important to understand how ridership is changing according to changes in service levels, population, and transit-dependent population, as these are the major factors tradition- ally influencing transit ridership. Therefore, for each mixed traffic and dedicated ROW cluster, a trend analysis was performed to examine the relationship between transit ridership and these three factors. In all cases, transit ridership was defined by unlinked passenger trips. Service levels are represented by transit vehicle revenue miles, although multiple similar measures were tested. Population is represented by one-year ACS estimates. Transit-dependent popula- tion is represented by zero-vehicle households from the ACS. Additional factors were consid- ered, but due to data limitations, these three were the most reliable across multiple regions. Appendix B clarifies the data limitations the study team faced in the analysis. With regard to transit vehicle revenue miles, other service level variables were considered, but all service level variables were very closely linked, leading the study team to conclude that only one was necessary for further analysis. Transit Agency Strategies and Case Study Selection There is little existing peer-reviewed research on strategies that transit agencies have taken to combat the declines in transit ridership. Therefore, news articles and transit agency reports were examined to get a picture of strategies being undertaken and the degree to which they have been successful. Taking into account the transit ridership trends, the factors influencing those trends, and the strategies transit agencies are using to combat ridership change, ten transit agencies were selected to conduct case studies. Table 4 lists the ten transit agencies and their associated clus- ters for mixed traffic modes and dedicated ROW modes. Five of the transit agencies have both dedicated ROW and mixed traffic modes, all five mixed traffic mode clusters are represented, and all dedicated ROW clusters except Los Angeles are represented. Transit Agency City Mixed Traffic Cluster Dedicated ROW Cluster Connect Transit Bloomington–Normal, IL 2 N/A IndyGo Indianapolis, IN 2 N/A Pinellas Suncoast Transit Authority St. Petersburg, FL 2 N/A Spokane Transit Authority Spokane, WA 2 N/A Greater Portland Transit District Portland, ME 3 N/A Maryland Transit Administration Baltimore, MD 1 C Metro Transit Minneapolis–St. Paul, MN 1 E Metropolitan Transit Authority of Harris County Houston, TX 4 E Massachusetts Bay Transportation Authority Boston, MA 5 B King County Metro Seattle, WA 5 D Table 4. Case study transit agencies.