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11 Chapter 2: Research Approach 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 strategies may be effective at offsetting ridership declines. While the overall ridership trend is pointing 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 effectively 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 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 therefore necessary to compare the evolution of transit ridership over time. Therefore, 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. The clustering method is explained below. 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 publication titled Comparing Transit Agency Peer Groups using Cluster Analysis by Ederer, et al. Transit regions were clustered into groups of peers on the basis of metropolitan area population, percent of population living in a dense area, percent 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 right-of-way. The mixed traffic and dedicated right-of-way mode categories were separated based on National Transit Database data. ï· Mixed traffic regions included all metro areas operating intra-city bus, commuter bus, bus rapid transit, and streetcar service.
12 ï· Dedicated right-of-way modes included heavy rail, light rail, monorail, and hybrid rail. Dedicated right-of-way services only included systems with 1 million or more unlinked passenger trips per year. Transit agencies that operate mixed and dedicated right-of-way 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 different 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 metropolitan or micropolitan statistical areas. This unit was chosen as it has the most data availability for any regional metric from the US Census. American Community Survey 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 Transportation Association. 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 â 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. ï· Cluster 1 â Mid-sized, transit-oriented â Features older industrial cities that are typically in 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. Example cities include Albany, Baltimore, Pittsburgh, and Cleveland. ï· Cluster 2 â Mid-sized, auto-oriented â Features primarily smaller, recently developed cities in the Midwest and South with low percentages of people living in zero vehicle households. Example cities include Indianapolis, Kansas City, Charlotte, and Nashville. ï· Cluster 3 â Sprawling small towns â Consists of the smallest cities operating fixed route transit service and includes a disproportionate number of âcollege towns.â The metro areas in this cluster are the least dense, least populated, and spend the least on transit of the transit
13 agencies included in this analysis. Example cities include Lansing, Burlington, Blacksburg, and Knoxville. ï· Cluster 4 â Sprawling metropolis â 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. Example cities include Atlanta, Houston, Denver, and Phoenix. ï· Cluster 5 â Dense metropolis â Consists of the largest metro areas in the county. 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. FigureÂ 4:Â MapÂ ofÂ MixedÂ TrafficÂ TransitÂ RegionsÂ byÂ ClusterÂ Clusters â Dedicated Right-Of-Way Modes The resulting dedicated right-of-way clusters are described below. Figure 5 below delineates the clusters for metropolitan areas operating dedicated right-of-way services with at least 1 million trips per year.
14 ï· Cluster A â Los Angeles â 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 right-of-way service. ï· Cluster B â Dense metropolis â Includes Chicago, Boston, Philadelphia, San Francisco, and Washington D.C. These are large metro areas with extensive transit systems and large commuter rail networks. ï· Cluster C â Mid-sized, dense â 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. ï· Cluster D â Mid-sized, dense, auto-oriented â 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 percentages of zero vehicle households, but a high proportion of population living in dense census tracts. ï· Cluster E â Sprawling metropolis â 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, Clusters D (Mid-sized auto- oriented) and Cluster E (Sprawling Metropolis) are more closely related to each other than the other clusters, and so on.
15 FigureÂ 5:Â DendogramÂ ofÂ DedicatedÂ RightâofâWayÂ ClustersÂ Ridership Trends As the major factors traditionally influencing transit ridership, it is important to understand how ridership is changing according to changes in service levels, population, and transit-dependent population. Therefore, for each mixed traffic and dedicated right-of-way 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 (UPT). Service levels are represented by transit vehicle revenue miles (VRM), although multiple similar measures were tested. Population is represented by one-year American Community Survey (ACS) estimates. Transit-dependent population is represented by zero-vehicle households from the ACS. Additional factors were considered, 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. E A B C D
16 Taking into account both the transit ridership trends and 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 1 lists the ten transit agencies and their associated clusters for mixed traffic modes and dedicated right-of-way modes. Five of the transit agencies have both dedicated right-of-way and mixed traffic modes; all five mixed traffic mode clusters are represented; and all dedicated right-of-way clusters except Los Angeles are represented. TableÂ 4:Â CaseÂ StudyÂ TransitÂ agenciesÂ 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