Ridership Study A Co-indie sample project December 1st, 2023
Table of Contents Introduction 1 Literature Review 2 Transit Ridership Trends 3 Data and Methods 4 Determinants of Ridership 6 Key Takeaways 11
Introduction
A rider prepares their mobile fare payment before boarding. In the past decade, mobile fare payment systems have increased in popularity.
During the past decade, transit ridership in the United States has peaked and troughed. These trends have been the subject of much academic, market, and agency-level research aimed at pinpointing the reasons for decline in a country where economic growth—which was tied to transit ridership levels prior to 2014—has accelerated considerably (Diffee, 2018). The current downward trend in ridership generally began after 2014, when most transit agencies recorded a peak in ridership (Grisby et al., 2018). The purpose of this literature review is to provide a broader context in which to situate our own research on transit ridership trends in downstate Illinois. Understanding trends in other places will help guide our efforts to explore the extent of, reasons for, and solutions to decreasing transit ridership in Illinois regions. In this chapter, we review the body of literature that analyzes the main factors behind transit ridership trends in the U.S. We begin by evaluating overarching trends in ridership over recent decades and exploring the various methods that researchers have used to analyze ridership data. The existing literature on transit ridership points to several possible reasons for decline in public transit demand, which we discuss in the following section. To provide a more granular and place-specific perspective on ridership decline and its implications, we summarize two recent reports on transit trends in California regions. Finally, we discuss some possible ramifications of the COVID-19 pandemic on transit ridership in the short- and long-term future. The literature reviewed for this effort includes a mix of peer-reviewed journal articles, reports from public agencies, books, and technical reports. To fully contextualize the story of transit ridership in downstate Illinois, we examined existing studies in various locations across the U.S., and with diverse units and levels of analysis ranging from agencies, to regions, to the nation as a whole.
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Literature Review Citation resource
Location
Unit of analysis
Data used
Findings
Alam, B. M., Nixon, H.,& Zhang, Q. (2018)
United States
MSA
Census Bureau, NTD
Internal factors, such as operating hours and headways, tend to be better predictors of transit demand than external factors
American Public Transportation Association (2019)
United States
Transit agency
NTD
Total number of rail systems is expanding and public transit ridership has seen a recent decline
Berrebi, S. J., et al. (2019)
Portland, OR, Miami, FL, Minneapolis, MN
Transit agency
Automatic passenger Ridership depends on counters (APC), Census frequency of service; busy Bureau, GTFS routes drive down ridership
Boarnet, M. G., et al. (2018)
Los Angeles
MSA
ACS
Ridership decline especially affects places where white, educated, carless people live
Boisjoly, G., et al. (2018)
United States and Canada
Transit agency
NTD, Canadian Urban Transit Association (CUTA)
VRK and increased car ownership are found to be the main causes of declining transit ridership
Brown, A., et al. (2016)
United States
National
NHTS
Public transit reliance decreases with age. Ridership among millenials is higher than that among older generations
Carrel, A., Halvorsen, A., Walker, J. L. (2013)
San Francisco, CA
Transit agency
Online survey
Delays, waits at transfer points, and crowded vehicles significantly drove down ridership
Driscoll, R. A., et al. (2018)
United States
National
ACS, NHTS
Aging population puts downward pressure on transit ridership
Shaheen, S., Cohen, A. (2018)
United States
National
Interviews
MaaS (Mobility as a Service) is contributing to declining public transit ridership
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Transit Ridership Trends Transit
Transit ridership has been declining across the U.S. since around 2014. Total unlinked transit passenger trips fell by 7.5% nationwide between 2014 and 2019; when New York City is excluded from this measure, nationwide ridership declined by 9% from 2008 to 2018 (American Public Transportation Association 2019). Though the national metric for transit ridership shows an overall decrease, breaking the metric down by mode reveals further detail: between 2014 and 2018, bus ridership decreased by 12% while rail only decreased by about 3%. Separating ridership measures by population size also yields varying results. In MSAs with more than 5 million residents, ridership fell by 7% from 2014 to 2018. During the same period, ridership dropped by 12.5% in MSAs with 1-5 million residents and by 7% in MSAs with less than 1 million residents. In cities with legacy transit systems (New York, Chicago, Philadelphia, Washington DC, Boston, and San Francisco), ridership dropped by 5.4% between 2014 and 2018 (National Transit Database 2018). Defying these national trends, ridership is increasing in a few U.S. cities: between 2016 and 2017, ridership increased in the Seattle MSA by 3%, in the Phoenix MSA by 2.7%, and in the New Orleans and Salt Lake City MSAs by less than 1% (National Transit Database 2018). Declining ridership has impacts on transit agencies, communities, riders, and people who are transit-dependent. Instead of starting conversations about how to make public transit more competitive with other modes, ridership decline is often the impetus for discussions about service cuts. For riders, and especially transit-dependent ones, reduced service can lead to job, housing, and food insecurity. Exploring the extent of and reasons for ridership decline can help agencies respond to trends without putting riders at risk of reduced mobility.
Transit ridership trend in large urban areas in the US, 2000-2020
5 billion
4.5 billion
4 billion
3.5 billion
3 billion
2.5 billion 2000
2005
2010
2015
NY
Top 10 UA
Top 100 UA
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Data and Methods Despite the general national trends and impacts noted above, existing studies show great variation in recent transit ridership trends. Every snapshot of transit ridership reflects the unique characteristics of the particular transit mode, place, and time period that the research surveyed. For example, Ederer et al. (2019) found that ridership trends differed even among cities with similar characteristics. More specifically, the study was not able to establish a relationship between population growth or service levels and ridership in an analysis of groups of MSAs with similar population sizes, zero-vehicle household shares, and transit agency operating expenses. Variations also exist in ways of measuring decline. A recent survey of commuters by the American Public Transportation Association (APTA) found that the share of workers commuting by transit fell to 5% in 2017, which was down 0.23 percentage points from its high in 2015. Meanwhile, Berrebi et al. (2019) analyzed measures of unlinked passenger trips nationally, revealing that in 2017, after five consecutive years of decline, bus ridership reached its lowest point since 1965. Yet, some other studies use a different measure, passenger miles traveled (PMT), and present a slightly different pattern (Mallet, 2018). Most of these studies show ridership decline, but the extent and pace of the decline varied, depending on the measures, data, and methods used. To further discuss the diversity of approaches to transit ridership research, we will now discuss some of the methods for studying ridership trends, including potential data sources, collection methods, and statistical analysis tools. Data facilitates understanding of ridership demand across time and space, but the ways it is used can also cause discrepancies in findings. Most nationallevel figures are sourced from the same handful of databases. The bulk of the data used in the research that we summarized was derived from the Federal Transit Administration (FTA)’s National Transit Database (NTD), which was established by the U.S. Congress to be the country’s primary source for data, information, and statistics on transit systems nationwide. NTD data are used to allocate federal funds to individual transit agencies, and all transit agencies receiving federal funding must report their annual data to the NTD (Florida
Data plays a key role in public transit ridership research. Transit agencies collect data from current riders and use it to create models of potential future scenarios. Researchers often collect data from lapsed transit riders to learn about ways that agencies can improve.
Department of Transportation, 2014). APTA releases annual reports based on NTD data that summarize ridership across the U.S. by mode for member agencies. The Census Bureau’s Longitudinal Employer Household Dynamics (LEHD) data, as well as third-party websites TransitFeeds and General Transit Feed Specification (GTFS) Data Exchange, also provide valuable data that informs the literature. Various types of statistical analysis have been employed in investigations of ridership data. Most national studies use regression models that attempt to associate transit ridership or its change over time with various internal and external factors across metropolitan statistical areas (MSAs) or transportation agencies. Alam et al. (2018) used a log-log regression approach to analyze a sample of 358 MSAs across the U.S. in order to identify common factors that are relevant to the national trend. Using a compilation of data from 25 transit authorities across the U.S. and Canada, Boisjoly et al. (2018) selected a longitudinal multilevel mixed-effects regression method. The researchers used ridership as their dependent variable in order to study possible determinants of downward trends in transit demand. For comparing ridership data across unique geographies, a fixed-effects model can be used. Berrebi et al. (2019) also used a fixedeffects model but at the route-segment level (a cluster
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Data and Methods of seven adjacent stops) to explain the relative change in transit ridership for transit agencies in Portland (OR), Miami, and Minneapolis-St Paul. A fixed-effects model mitigates irregularities and variations to focus only on the relationship between ridership change and changes in explanatory factors. Berrebi and Watkins (2020) utilized a Poisson fixed-effects model to evaluate the relationship between neighborhood sociodemographic characteristics and ridership change over time, while controlling for changes in service frequency, population, and job availability. Because of their ability to isolate key features of datasets and mitigate unnecessary error or “noise,” statistical tools play a large role in comparing ridership trends across compilations of cities, agencies, or regions. Studies focusing on a single metropolitan area, such as the ones reviewed in section 5 (Manville et al., 2018, Wasserman et al., 2020), tend to employ a more descriptive statistical analysis for various sub-locations
Public transit riders walk across a platform as a train departs.
and population groups. However, most existing studies mainly rely on a quantitative analysis to test possible reasons for transit ridership decline. The research team was able to identify few research efforts to shed light on how these macro factors are reshaping individual travel decisions using qualitative data such as surveys and interviews of lapsed transit riders and non-riders. Declining ridership has impacts on transit agencies, communities, riders, and people who are transitdependent. Instead of starting conversations about how to make public transit more competitive with other modes, ridership decline is often the impetus for discussions about service cuts. For riders, and especially transit-dependent ones, reduced service can lead to job, housing, and food insecurity. Exploring the extent of and reasons for ridership decline can help agencies respond to trends without putting riders at risk of reduced mobility.
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Determinants of Ridership Much of the work done to understand the extent of ridership decline also delves into possible reasons for this trend. Like in the previous section, findings in this section include national shifts and granular snapshots of more specific places. The potential reasons for transit ridership decline can be divided into two categories: internal factors and external factors. Internal factors: • Relate to decisions, policies, and conditions determined by the transit agency or municipalities providing subsities (Boisjoly et al., 2018). • Can impact ridership by influencing the experience of using transit. • Include service reductions, funding, and fare changes. External factors: • Typically entail broader economic influences, such as unemployment rates and gas prices (Boisjoly et al., 2018). • Include increased automobile ownership, reduced gas prices, increased availability of mobile ride0ailing, and flexible teleworking schedules. • Contribute to cost- and time-effective alternatives to public transit.
Service reductions
The literature on transit demand has placed a particular focus on how service availability impacts ridership. Most researchers agree that increasing service induces an increase in ridership. However, transit funding strategies continue to be organized around proven demand rather than the potential for demand under increased service, a dynamic which places pressure on transit agencies to make do with fewer resources. Several multi-city, multi-agency transit ridership studies found that fluctuations in internal factors have played a part in the recent downward trend in ridership. Internal factors refer to aspects of service over which transit system managers have control (e.g., transit supply, route coverage). Alam et al. (2018), in an analysis of hundreds of American MSAs compiled to be generalizable to the country as a whole, refute literature stating that
socioeconomic factors, or external factors, contribute to transit demand. Of several factors known to affect transit demand, their research found that transit supply has the highest impact on travel demand. For bus transit in particular, a 10% increase in transit supply was found to be associated with a 5.75% increase in ridership. Also, for bus transit, a 10% increase in bus route coverage was associated with a 3.19% increase in ridership. Their study concludes that the job of building transit ridership belongs to policymakers and planners. Another study (Berrebi et al., 2019) in Portland, OR, Miami, FL, and Minneapolis-St Paul, MN found in all three cities that ridership depends on frequency of service and that chronically busy routes tend to experience declines in ridership over time. Bolstering these findings, a study of 25 transit agencies across the U.S. and Canada found that reductions in vehicle revenue miles (VRM) were a major contributor to bus and rail ridership declines (Boisjoly et al., 2018). Their analysis showed that a 10% reduction in VMR is associated with an 8.27% decrease in bus and rail ridership. An analysis across different types of metropolitan areas (Watkins et al., 2019) shows that the association between transit service cuts and transit ridership reduction is particularly strong in mid-sized, automobile-oriented metropolitan areas and sprawling small towns. Though not formal service reductions, operational deficiencies and safety issues in transit systems also lead to similar negative outcomes. These “informal” service reductions led to service disruptions and declined ridership in Washington, DC and New York City in recent years, according to data from the NTD (Malalgoda and Lim, 2019). The same research found that enhanced ontime performance, improved service and reliability, and better service quality and safety are important to riders and that the influence of these factors exceeded that of competition from transportation network companies (TNCs). An analysis of informal service reduction based on a survey of transit ridersin San Francisco, CA, showed that the most significant negative experiences that drove a reduction in transit use were delays perceived to be the fault of the transit agency, long waits at transfer points, and being prevented from boarding due to crowded platforms stops (Carrel et al., 2013).
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Funding and fares
Like ridership, funding for transit is decreasing nationally. According to the 2019 Public Transportation Fact Book, total national transit funding decreased by 1.4% to $71.19 billion and passenger fare revenue declined by 2.5% to $15.84 billion (American Public Transportation Association, 2019). As total funding drops nationwide, federal funding is becoming less of a resource for transit agencies. In recent years, the continued decline in federal funding has been attributed to lengthy and often uncertain disbursement schedules by the Federal Transit Administration (Streetsblog, 2018). Since 1997, the top capital funding source for transit agencies in the U.S. has shifted from federal to local. Local funding, which comes from sources such as passenger fare revenues,sales tax revenues, parking revenues, advertising revenues, and municipal bonds, is increasing to compensate for decreased federal funding. Of these local sources, the sales tax was by far the most popular voter-approved avenue for transportation funding in 2018, according to a report from the Eno Center for Transportation. The same report showed that less than 10% of funding was approved by statewide measures; the rest was approved by regional, county, or municipal governing bodies (Transportation at the Ballot Box, accessed September 20, 2020). In 2017, 36% of transit agency revenue was derived from passenger fare revenues. Decreasing ridership, which resulted in a 1.8% drop in passenger fare revenues from 2016 to 2017, places financial strains on transit agencies, which already struggle to turn a profit. Increasing fares, however, only worsens the problem: in a longitudinal analysis of public transit ridership in 25 North American cities from 20022015, Boisjoly et al. 2018 found that a 10% increase in fares is linked with a 2.19% decrease in ridership. Mallett (2018) pointed out that average fares have risen faster than inflation in recent years, potentially discouraging transit rides. In short, ridership is positively correlated with VRM and negatively correlated with fare increases (Graehler et al., 2019). This dynamic will continue to present significant challenges for public transit in the U.S. if federal funding continues to decrease.
Fare payment approaches can impact ridership. Offering more flexible fare payment removes barriers to mobility for vulnerable populations.
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Shared mobility and TNCs
Shared mobility includes all transit modes that are shared among users, either concurrently or one after another. Under the Shared-Use Mobility Center’s definition, public transit counts as shared mobility (Shared-Use Mobility Center, accessed May 13, 2020). Transportation network companies (TNCs) facilitate transportation services that are accessible to users on an as-needed basis. Examples of transportation services facilitated by shared mobility providers include bikeshare, scooter share, and ride-hailing. Access to such services is increasingly facilitated by technology, namely smartphones. Use of transportation services facilitated by TNCs is most common late at night and for social trips, and it is less common—but not uncommon—as a typical commute mode (Feigon and Murphy, 2016). Ride-hailing is undoubtedly the most popular service facilitated by TNCs, as it more than doubled the size of the overall forhire services sector between 2012 and 2017 (Berrebi and Watkins, 2020). In its exploration of the impacts of ride-hailing TNCs on public transit demand, the literature is inconsistent. Graehler et al. (2019) finds that the introduction of TNCs in an area results in significant decreases in rail and bus ridership that compounds over time, but that these external factors are not suitable explanations for the entirety of ridership decline. A research report from Clewlow and Mishra (2017) that details the results of a survey deployed in seven U.S. cities came to the specific conclusion that TNCs are associated with a 6% decrease in bus trips and a 3% decrease in light rail trips. However, the same report suggested that ride-hailing is also adding passenger miles to public transit in large cities. In other words, TNCs have been associated with a decline in demand of some transit modes but are not thought to be the sole cause, and in some cases, the presence of ride-hailing TNCs is likely supplementing public transit ridership. Other literature points to a conflicting narrative around TNCs. This literature suggests that instead of taking riders away from public transit, TNCs can actually supplement and complement public transit. This research characterizes shared mobility as part of a
transit-dependent lifestyle embodied by people who are likely to be younger, non-white, and residents of more urbanized areas. An online survey distributed to residents of Austin, Boston, Chicago, Los Angeles, San Francisco, Seattle, and Washington DC, found that shared mobility users rely on public transit more and own fewer cars compared to national averages (Feigon and Murphy, 2016). The survey results also revealed that shared mobility users ride public buses and trains more frequently than people who don’t regularly use shared mobility modes. Another investigation into the effects of TNCs, which used national data from the U.S. Department of Transportation and the National Transit Database, revealed that higher-than-average TNC use was correlated with higher-than-average rail ridership on an agency-level basis. In relation to bus ridership, however, the same research found that TNCs neither complemented nor replaced it (Malalgoda and Lim, 2019). Graehler et al. (2019) suggest that bike share could potentially complement transit by providing first and last-mile connectivity. Interestingly, bikeshare is associated with increases in heavy rail and light rail ridership, but with a decrease in bus ridership. Similar to the literature on TNCs and ridership, however, this finding about the potential influence of bikeshare on public transit ridership is also inconclusive. Under some circumstances, TNCs are associated with lower heavy rail and bus ridership and this association increases each year after TNCs enter a market. Findings from a recent report by Schaller (2018) suggested that if TNC services were not available, the majority of TNC customers would have used public transit or active transportation or would not made the trip at all. Further efforts to measure the relationship between TNCs of all types and public transit demand are needed.
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Vehicle ownership and gas prices
The cost of vehicle ownership has a substantial impact on transit ridership patterns. As the expenses associated with owning and maintaining a vehicle, such as fuel, insurance, and maintenance costs, rise, individuals may increasingly turn to public transit options as a more cost-effective alternative.
Most transportation infrastructure in the U.S. was built to accommodate private vehicles, which has made driving an automobile the most popular mode of transportation. The single largest predictor of whether an individual uses public transit is whether they have access to a private vehicle (Manville et al., 2018). Consequently, those unable to drive are much more likely than the general population to use public transit. While access to a motor vehicle is among the strongest reasons that transit ridership has been low in the U.S. compared to other countries, it is also thought to be part of the reason ridership is decreasing in recent years. Accessing motor vehicles, either to drive or be driven in, is becoming easier in the U.S. In a study of declining transit ridership in Southern California, Manville et al. (2018) found that the number of households with no vehicles decreased by 30% over a 15-year span. Notably, the same study found that among foreign-born residents, zero-vehicle households were down by 42%. The literature notes that living in a household with a vehicle may be the most compelling predictor of transit use, and that the sharp decrease in the number of these households in Southern California accounts for a large share of missing transit riders in recent years. Data from the National Household Travel Survey indicates that between 1969 and 2009, the share of households without a vehicle dropped from about 21% to about 9% and the average number of vehicles available per household increased from 1.16 to 1.86. A more recent report on U.S. household travel data from Sivak (2018) listed peak vehicles per household at 2.050 in 2006. Since then until 2016, the most recent year included in the report, the measure hasn’t decreased past 1.9. Trends in gas prices and car payments, two long-term financial obligations associated with owning a private vehicle, have facilitated access to driving as a primary transportation mode. Between 2014 and 2016, the price of gasoline fell from an annual average of $3.46 per gallon to $2.20 per gallon in 2016 inflation-adjusted dollars (Mallett, 2018). Between 2012 and 2016, average gas prices in the U.S. declined every year and new car loan interest rates never rose above 5%. Meanwhile, real median income increased by 10.8% for African American households and by 20.5% for Hispanic households (Berrebi and Watkins, 2020). In short, lower numbers of zero-car households and lower gas prices—both of which tend to be associated with lower transit use (McLeod et al., 1991; Taylor and Fink, 2009; Taylor et al., 2009)—are among the most likely explanations for recent declines in transit ridership. Importantly, immigration trends and policies could also
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have an influence on transit ridership: slightly more than one-quarter of states have eliminated restrictions on drivers’ licenses for undocumented immigrants, making it easier for an even greater share of the population to use personal vehicles as their primary mode of transportation (Manville et al., 2018). The dynamics of transit ridership in the United States are intricately tied to the economics of vehicle ownership. The costs associated with owning and operating a vehicle, spanning from initial purchase prices to ongoing expenses like fuel, insurance, and maintenance, exert a notable influence on individual transportation
choices. High ownership prices can serve as a deterrent, prompting individuals to explore alternatives, particularly in urban settings where public transit systems offer cost-effective and convenient options. As vehicle ownership becomes more financially burdensome, public transit becomes an appealing and pragmatic solution, providing an efficient means of transportation without the associated costs and responsibilities of owning a personal vehicle. Consequently, the interplay between the affordability of vehicle ownership and the accessibility of transit services plays a crucial role in shaping transportation behaviors and influencing the overall sustainability of urban mobility.
Car ownership by state, 2019
% no car 0%
40%
Vehicle ownership rates are highest in the Southeastern states and Great Plains.
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Key Takeaways This review confirms the presence of several internal and external factors that are together contributing to declining public transit ridership across the U.S. Beyond the overarching observation that public transit ridership is decreasing, the work of exploring transit trends yields unique results based on a number of place-specific characteristics and the choice of statistical tools. Findings regarding the extent of the decline vary widely, depending on the ridership statistics of specific geographies, the timeframe in question, and the characteristics of the riders. The reason for decline varies from study to study and often involves several factors, from the implementation of bikeshare infrastructure to gentrification around transit stops. Impacts of transit ridership decline also tend to be place-specific but generally include reduced service. Internal factors causing transit ridership decline include transit service cuts, decreasing transit funding, and rising transit fares. The literature also identifies external factors beyond the control of transit agencies, including emerging shared mobility and TNCs, rising vehicle ownership and declining gasoline prices, demographic shifts and intra-metropolitan migration away from transit cities, telecommuting and online shopping, and gentrification and displacement. Studies show that the association between transit service cuts and transit ridership reduction is particularly strong in mid-sized, automobile-oriented metropolitan areas and sprawling small towns. Case studies of large metropolitan areas tend to focus on the changing location and travel choices of transit-dependent populations, such as zero-vehicle households, lowincome groups, and immigrants. Despite inevitable inconsistencies in research conclusions, there are several solvable gaps in the literature on ridership decline. In particular, analyses of bikeshare and overall TNC impactsstill yield consistently inconclusive results. Evidence of poor service and funding reductions, however, almost invariably results in decreased ridership. Transit agencies and municipalities seeking to rebuild ridership should consider improving their service and curtailing fare increases. The impact of COVID-19 on transit ridership has been
dramatic, widespread, and fairly consistent across agencies. The characteristics of past intersections between public health and transit are markedly different from those of COVID-19 and transit in 2020. Because COVID-19 is global and highly communicable, researchers and transit agencies are navigating this crisis with very little prior experience, which makes the future of transit ridership uncertain. While nearly all transit agencies are experiencing dramatically reduced transit ridership and fare revenues, safe transit operation during the pandemic adds huge additional costs. To overcome the financial strain, most agencies are shifting some funds from capital to operating expenses, which might affect agencies’ capacity to provide appropriate levels of transit service in the future.
Ridership Study A Co-indie sample project December 1st, 2023
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