Literature Review

IDOT NON-RIDER STUDY - LITERATURE REVIEW INTRODUCTION 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. Table 1 shows each of the studies included in this review along with some of their key characteristics and findings.

Table 1. Studies Included in the Literature Review Citation Alam, B. M., Nixon, H., & Zhang, Q. (2018) American Public Transportation Association (2019)

Resource type Peer-revi ewed article

Location of study United States

Unit of analysis MSA

Key measure(s) Unlinked passenger trips

Data used

Key findings

Census Bureau, NTD

Public agency report

United States

Agency

Unlinked passenger trips

NTD

Internal factors, such as fares, operating hours, and headways, tend to be better predictors of transit demand than external factors Total number of rail systems is expanding and public transit ridership has seen a recent decline

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Citation

Resource type Peer-revi ewed article

Location of study Portland, OR, Miami, FL, Minneapolis-St. Paul, MN

Unit of analysis Transit agency

Key measure(s) Boardings per trip

Data used

Key findings

Automatic passenger counters (APC), Census Bureau, GTFS ACS

Ridership depends on frequency of service; busy routes drive down ridership

Berrebi, S.J., & Watkins, K.E. (2020)

Peer-revi ewed article

Route segment

APC, GTFS, ACS

Boarnet, M. G., et al.

(2018)

Public agency report

Portland, OR, Miami, FL, Minneapolis-St. Paul, MN, Atlanta, GA Los Angeles

MSA

Move patterns near rail stations

California Franchise Tax Board (FTB) data

Neighborhoods with new stations had more active move patterns than those without new stations

Boarnet, M.G., et al. (2017)

Public agency report

Los Angeles

MSA

Move patterns near rail stations

California Franchise Tax Board (FTB) data

After new station openings, fewer poor residents moved into the area

Boisjoly, G., et al. (2018)

Peer-revi ewed article

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

Peer-revi ewed article

United States

National

NHTS

Public transit reliance decreases with age. Ridership among millennials is higher than that among older generations. This isn’t likely to persist as millennials age unless transit service (frequency, reliability) improves.

Carrel, A., Halvorsen, A., & Walker, J. L. (2013)

Peer-revi ewed article

San Francisco, CA

Transit agency

Vehicle Revenue Kilometers (VRK), fares, ridership Percent of adults who used public transit (1) on survey day and (2) in the past month Frequency of trips (trip log)

Brown, A., et al. (2016)

Online survey

Delays, waits at transfer points, and crowded vehicles significantly drove down ridership

Driscoll, R. A., et al. (2018)

Peer-revi ewed article

United States

National

Unlinked commute trips

ACS, NHTS

Aging population puts downward pressure on transit ridership

Ederer, D., et al. (2019)

Peer-revi ewed article

United States

MSA

Unlinked passenger trips

Census Bureau, NTD

There is variance in determinants of ridership decline, even among clusters of cities with similar characteristics

Berrebi, S. J., et al. (2019)

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Ridership decline especially affects places where white, educated, carless people live

Citation Feigon, S., & Murphy, C. (2016)

Resource type Peer-revi ewed article

Location of study Austin, TX, Boston, MA, Chicago, IL, Los Angeles, CA, San Francisco, CA, Seattle, WA, Washington, Discrict of Columbia, United States

Unit of analysis MSA

Key measure(s) Shared mobility use

Data used

Key findings

Online survey

Transit agency performance metrics need to be shifted to better compete with shared mobility

Transit agency

Unlinked passenger trips

NTD, ACS, U.S. Energy Information Administration, Uber

Service levels, gas prices, and vehicle ownership aren't suitable explanations for the whole of ridership decline

Population, gas price, real income, VMT, traffic congestion index Bus ridership over 12 months

APTA

Reasons for ridership decline include erosion of time competitiveness and cost effectiveness, as well as TNCs

Metropolitan Transit Authority of Harris County

Low-to-medium levels of ridership are positively correlated with crime

Graehler, Michael & Mucci, Alex & Erhardt, Gregory.

(2019) Grisby, D., et al. (2018)

Peer-revi ewed article

Public agency report

United States

MSA

Li, Q., et al.

(2019)

Peer-revi ewed article

Houston, TX

Transit agency

Malalgoda, N., & Lim, S. H. (2019)

Peer-revi ewed article

United States

Transit agency

Unlinked passenger trips

NTD, Bureau of Economic Analysis

TNCs neither supplement nor substitute bus transit; TNC availability increased rail ridership

Polzin, S. E., Godfrey, J., & Center for Urban Transportation Research.

(2019)

Technical report

Florida

National and transit agency

Trips per capita

NHTS

Shaheen, S., & Cohen, A. (2018)

Peer-revi ewed article

United States

National

Impacts of mobility on demand on transit agency operations

Interviews with transportation professionals

Florida's ridership decline is more severe than the national average.

Meanwhile, Florida has one of the lowest zero-vehicle household shares of any U.S. state and such households are less likely to use transit than zero-vehicle households nationwide.

MOD (Mobility on Demand) and MaaS (Mobility as a Service) are contributing to declining public transit ridership

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Citation

Location of study San Francisco Bay Area

Unit of analysis MTC region

Key measure(s) Unlinked passenger trips

Data used

Key findings

Wasserman, J. (2019)

Resource type Technical report

NTD, NHTS, agency data

Wasserman, J., et al. (2020)

Technical report

San Francisco Bay Area

MTC region

Unlinked passenger trips, boardings per capita

NTD, NHTS, agency data

Station-area employment was found to have a strong influence on ridership. Concentrated employment clusters tend to attract riders.

Bay Area ridership peaked in 2017 and is now facing patronage losses due to demographic changes in transit-rich areas and residential shifts away from work centers.

TRANSIT RIDERSHIP TRENDS 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).

Figures 1 through 3 show transit ridership trends in the most populous 100 urbanized areas in the U.S. When excluding the New York City urbanized area, which accounts for about 40% of national transit ridership, the nationwide decline began in 2008 and has accelerated since 2014 (Figure 1).

Figures 2 through 3 show different ridership trends by mode: while bus ridership began to decline in 2008 nationwide, rail transit ridership sustained growth until the 2014 peak and then began to decline.

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Figure 1. Transit ridership trend in large urban areas in the US, 2002-2017 Source: National Transit Database

Figure 2. Bus transit ridership trend in large urban areas in the US, 2002-2017 Source: National Transit Database

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Figure 3. Rail transit ridership trend in large urban areas in the U.S., 2002-2017 Source: National Transit Database 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.

DATA AND METHODS USED TO STUDY RIDERSHIP DEMAND 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,

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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 national-level 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 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 fixed-effects model but at the route-segment level (a cluster 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 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.

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DETERMINANTS OF TRANSIT RIDERSHIP: INTERNAL AND EXTERNAL FACTORS 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. We divide the potential reasons for transit ridership decline into two categories: internal factors and external factors.

Internal Factors Internal factors relate to decisions, policies, and conditions determined by the transit agency or the municipalities providing subsidies (Boisjoly et al., 2018). Such factors, which range from route optimizations to rebranding, can impact ridership by influencing the experience of using transit.

Several recent studies show that service reductions and funding and fare changes are among the internal factors that caused the recent transit ridership decline.

Service reduction 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

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the NTD (Malalgoda and Lim, 2019). The same research found that enhanced on-time performance, improved service and infrastructure 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). Another analysis of informal service reduction based on a survey of transit riders in 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 because of crowded platforms and stops (Carrel et al., 2013).

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 2002-2015, 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.

External Factors External factors typically entail broader economic influences, such as unemployment rates and gas prices, that are beyond a transit agency’s control (Boisjoly et al., 2018). The 2019 Public Transportation Fact Book lists increased automobile ownership, reduced gasoline prices, increased availability of mobile ride-hailing, and flexible teleworking schedules as likely reasons for declining public transit demand. All of these factors affect transit decisions and possibly contribute to cost- and time-effective alternatives to public transportation.

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Shared mobility and transportation network companies 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 for-hire 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 for 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 firstand 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

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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.

Vehicle ownership and gasoline prices 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 use 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 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).

Demographic shifts Demographic shifts have been found to influence ridership trends as well. Small- and medium-metro counties across the U.S., which is where just over half of Americans live, have undergone extensive demographic change over the past 20 years. A report from the Pew Research Center (2018) found 11

that the share of white residents in small- and medium-metro counties decreased from 76% to 68% between 2000 and 2016. During the same timeframe, the share of immigrants in these counties increased from 8% to 11% and the share of residents aged 65 and older increased from 12% to 15%.

Also explored in national-level studies is the impact of age on a person’s propensity to regularly use public transit. Brown et al. (2016) found consistent evidence that reliance on public transit decreases with age, and that residence in densely populated and transit-rich areas is most common among young people. People of color, who tend to rely more on public transit, have a lower average age than white residents and tend to live in more transit-rich areas. But residence in such areas is unlikely to persist past youth and young adulthood, the research found. Essentially no statistically significant independent effects of generational birth cohort on transit use were observed (Brown et al., 2016).

The U.S. has a high average age relative to other countries, which is likely contributing to decreased overall mobility (Driscoll et al., 2018).

Transit use tends to increase from the teens into the mid-20s, but then declines until about age 70, where it levels off. Nearly 20% of millennials live in the 10 largest MSAs, which have the highest level of transit supply, compared with about 18% of older adults (Brown et al., 2016). As Baby Boomers approach retirement age, they also approach an age typically characterized by fewer trips taken per day and a decreasing share of trips taken via transit (Driscoll et al., 2018).

Population decline in transit cities Driscoll et al. (2018) notes declining overall population in counties with ample transit service and use, while population is growing in counties with lower levels of transit service. As U.S. suburbs become more accessible and attractive to historically city-dwelling populations, many metropolitan areas are experiencing population decline in their central locations that are better served by transit. As a result, declining transit ridership can be observed for the whole metropolitan area.

Transit is heavily used by immigrants, particularly during their first years in the U.S. However, immigration has slowed in recent years, especially to large cities with robust transit systems, which could also be a factor in the diminishing number of new transit users (Manville et al., 2018).

Telecommuting and changes in non-work travel Alternatives to in-person work, such as telecommuting, may reduce the demand for everyday commutes on public transit (Shaheen and Cohen, 2018). Between 2012 and 2016, the share of U.S. employees working remotely at least part-time rose by 4%, and the amount of time U.S. employees spent telecommuting also increased (Berrebi et al., 2019). Thirty-seven percent of workers report they telecommuted in 2015, which was up from 32% in 2006 and 9% in 1995 (Jones, 2015).

In addition to the spread of telecommuting, people are increasingly completing everyday activities online, such as grocery shopping, doctor’s visits, and other purchases. The degree to which e-commerce may reduce travel demand will likely be influenced by the continued evolution of online shopping experiences. Shaheen and Cohen (2018) suggest that e-commerce might lead to growth in emerging package delivery modes such as courier services and drones. They warn of the substitution of shopping trips for deliveries using emerging technologies and call for more research on the impacts

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of e-commerce on transportation. In sum, work commutes and daily shopping errands comprise a significant proportion of person trips; the subtraction of these trips from total trips is likely to affect public transit ridership, especially as more activities migrate to the internet.

Displacement and gentrification Tracking in- and out-migration in neighborhoods with robust or burgeoning public transit infrastructure sheds light on how people in different demographic groups choose to travel. For example, in a longitudinal study of households in Los Angeles, Boarnet et al. (2018) found that after a new metro station opened, an increasingly smaller share of extremely poor residents moved into the area. Another longitudinal investigation of gentrification and rail transit in Los Angeles County suggested that transit-proximate neighborhoods have much higher turnover rates than the national average. Meanwhile, higher-income neighborhoods exhibit lower-than-average mobility rates, which indicates a greater level of housing and financial security (Boarnet et al., 2017). In longitudinal studies of Portland, OR, Miami, FL, Minneapolis-St Paul, MN, and Atlanta, GA, the socio-demographic groups least likely to ride the bus were found to be responsible for the most ridership decline. In addition, ridership decline happened at a faster rate in places with fewer transit-dependent residents (Berrebi and Watkins, 2020).

Demographic variables associated with gentrification can help explain ridership change. In Portland (OR) and Miami, an increase in white residents was associated with ridership decline. In Minneapolis, an increase in residents without high school degrees was associated with ridership increase (Berrebi et al., 2019). This study of Portland, Miami, and Minneapolis concluded that economic displacement of transit-dependent residents may be causing ridership to decline in all three cities.

Transit-oriented developments (TODs), a popular planning tool to promote public transit and sustainable development, are increasingly attracting high-income residents from suburban areas because of their convenience and rich amenities. An influx of wealthy residents back into central-city neighborhoods may actually reduce ridership levels, however, since these high-income workers tend to use transit less than the lower-income residents they displace. Additionally, lower-income households are gradually moving to more suburban areas where public transit opportunities are less available (Manville et al., 2018). Chapple and Zuk (2020) found that TODs increased the likelihood of losing low-income households, where loss of low-income households was used as a proxy for displacement.

While existing studies reviewed in this section enhanced our understanding of reasons for the recent ridership decline, most studies only use quantitative evidence to explain the effects of various urban phenomena on ridership. Lapsed riders comprise a key demographic that holds many insights for analysts of ridership decline. Future research on the causes for and effects of ridership decline should harness the potential of qualitative data, including surveys and interviews of lapsed riders, to substantiate what quantitative figures alone cannot.

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PLACE-SPECIFIC REPORTS AND CASE STUDIES This section summarizes two case studies that exhibit in-depth analyses of ridership change. These case studies explored potential causes of ridership decline that consider demographic shifts and geography, factors which are often overlooked yet highly influential on transit trends.

An analysis of Southern California’s transit ridership trends and determinants was conducted by Manville et al. (2018). Their research aimed to determine which factors contribute to falling transit ridership numbers in the region. Overall, the report revealed evidence against common assumptions about the causes of declining transit ridership, including the hypothesis that shared mobility apps have siphoned riders away from public transit. Conclusions are based on a survey that asked Uber passengers in Southern California about their use of public transit. According to the findings, the average Uber passenger in Southern California has likely never used a bus and has never considered doing so. Rather, some evidence is consistent with the idea that neighborhood demographic change has been associated with less transit use. For example, research areas that were heavily populated with transit commuters in the year 2000 became, in the next 15 years, slightly less poor, and significantly less foreign-born. More evidence of demographic change, or possibly gentrification, is suggested in the following findings: between 2000 and 2010 in Southern California, the people most likely to use transit moved away from transit-rich areas, poverty rates in transit-rich areas fell by four percentage points, and the share of foreign-born residents fell by 3 percentage points (Manville et al., 2014).

Other evidence from this study supports the hypothesis that declining transit use is associated with increased access to personal vehicles. For instance, between 2000 and 2015, the share of households in the region with no vehicles fell by 30%. Additionally, among foreign-born residents, zero-vehicle households were down by 42% and those with fewer vehicles than adults were down 22%. To summarize these findings, Manville et al. (2018) stated that living in a household without a vehicle is possibly the strongest single predictor of transit use (or lack thereof) in Southern California.

An account of ridership in the San Francisco Bay Area by Wasserman (2019) compared trends across the region’s multiple transit agencies. Population in the Bay Area has increased by 11% between 2008 and 2017, but ridership dropped from 72 to 66 trips per person, per year. Thus, as the region gains more residents, those residents are riding public transit less. Muni and BART, two transit operators in the region, carried over 70% of 2017 regional ridership. On BART, weekday trans-Bay trips into downtown San Francisco have increased, while most other trip types have lost a significant share of riders. Contrary to trends in Southern California and numerous transit agencies across the U.S., the Bay Area’s most urban and frequently used train lines have actually gained riders. This is thought to be due to concentration of employment in downtown San Francisco and is an indicator of the importance of public transit to daily work commutes.

Like Manville et al. (2018), this work also considered place-specific demographics in its analysis. Since California has a substantial Hispanic population and Hispanic residents exhibit a high average transit use in the region, this demographic group is heavily influential on transit ridership. Although the Hispanic population increased slightly between 2009 and 2017, transit use among this group in the central Bay Area dropped nearly 22% in the same timeframe. This resulted in a loss of close to 4% of 14

total ridership in the Bay Area. Meanwhile the proportion of Bay Area residents with automobiles grew between 2009 and 2017 (Wasserman, 2019).

These region-specific studies expanded our understanding of recent transit ridership trends beyond what national cross-sectional studies presented. Nevertheless, these case studies also heavily relied on a quantitative analysis and hence cannot fully elucidate exactly how recent socioeconomic and technological trends are reshaping individual travel decisions.

EFFECTS OF COVID-19 ON TRANSIT RIDERSHIP AND OPERATIONS

The COVID-19 pandemic has impacted transit agencies and ridership trends significantly. Transit agencies have amended boarding protocol and adjusted service to uphold social distancing measures and meet lower levels of demand. The immediate effects of the pandemic on ridership trends are stark and demonstrate a logical societal response to evidence that shared occupation of small, indoor spaces increases the risk of disease transmission. A report released by a data analysis firm in May 2020 showed that ridership across all agencies and modes was down by 73% (EBP 2020).

Mode-specific statistics are just as dramatic: commuter rail ridership in New York City, Chicago, Philadelphia, and DC metro areas was down in excess of 90% in April 2020. Heavy rail ridership in New York City, San Francisco, and Los Angeles was down in excess of 75%. Bus ridership in Los Angeles, DC, and St. Louis, maintained about two-thirds of its baseline through April 2020 (Eno Center for Transportation, 2020). Despite the unsurprising immediate consequences of a pandemic on public transit, projections of longer-term and even post-COVID-19 ridership trends are unclear.

The novel characteristics of COVID-19 decrease the translatability of prior research to current conditions. However, some general findings from recent (but pre-COVID-19) epidemiological studies can inform transit agencies’ best practices during a pandemic. Research into changes in transmissibility of various communicable diseases on public transit has debunked common wisdom that limiting travel to only susceptible carriers (and prohibiting it among known carriers) almost fully prevents outbreaks on public transit. Rather, Xu et al. (2013) concluded that discouraging unnecessary travel among the entire public is proven to greatly reduce outbreaks stemming from spaces of public transit. Instituting travel limitations can be precarious however, due to the difficulty of accurately identifying known carriers. Additionally, myriad ethical implications result from limiting access to such a vital public resource. Given the challenges of hard and fast restrictions to public transit access, researchers suggest increasing transportation efficiency and improving on-board sanitation and ventilation to reduce the likelihood of on-board transmission (Xu et al., 2013).

Transit agencies are going to great lengths to ensure the safety of riders. Many agencies have purchased (and are continuing to purchase) extra supplies, such as masks, wipes, and hand sanitizer.

Additional vehicle and facility cleanings have become essential and providing educational safety training to employees and riders is also common. Investing so many extra resources is a major financial strain, however, especially when ridership is down an average of 70% nationwide. In a late-March survey of transit agencies across the country, the American Public Transportation Association (APTA) found that 98% of responding agencies had invested in increased cleaning, 95% had purchased extra supplies, 98% had provided employees with training, and 94% had disseminated

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messaging to the public (APTA Impact Policy Brief). A subsequent survey deployed by APTA in late April determined that, in order to continue to provide safe service and avoid layoffs, 83% of responding agencies were using their CARES Act funding. Other findings from the same survey further evidenced the financial strain that the pandemic has placed on transit agencies: 84% of responding agencies had shifted funds from capital to operating, and almost two-thirds had stopped collecting fares (APTA Continuing Impacts Policy Brief).

Among the many adjustments transit agencies have made to increase the safety of travel during the pandemic, most have implemented ways for riders to travel with increased personal space. This best practice echoes guidance from public health organizations to maintain six feet of distance between people of separate households while in public spaces. For transit agencies and transportation businesses, conforming to this practice has resulted in fewer available seats on buses, trains, and airplanes. However, demand for these seats is typically even less. In an ongoing account of transit trends in Swiss cities that began in September 2019, researchers found that, beginning in mid-March of 2020, bus, train, and tram usage dropped nearly 80% and bicycle usage increased by over 70%, as compared to averages from previous months. Travel data revealed that the average total daily distance traveled had dropped nearly 60% by March 30, 2020 (MOBIS Covid19 Mobility Report).

Data from a COVID-19 Impact Analysis Platform revealed similar behavior in Illinois: between March 1, 2020 and May 1, 2020, the share of people staying home shifted from a baseline share of no more than 20% on weekdays to several consecutive weeks with shares above 35% on weekdays and around 50% on weekends. Meanwhile, the average number of miles traveled per person per day dropped from 40.1 on March 1, 2020 to 33.4 on May 1, 2020. Both of these metrics are gradually trending towards their pre-COVID levels again (COVID-19 Impact Analysis Platform). According to an Illinois Mobility Report from Google, as of September 20, 2020, traffic at Illinois transit stations, including CTA, bus, and train stations statewide, is down 20% compared to baseline levels (COVID-19 Community Mobility Report). This is up significantly from 33% down in July and over 50% down during March and April.

Though decreasing overall ridership trends are nearly universal, they do not tell the full story of COVID-19 and public transit. A survey of 25,000 users of the Transit mobile app found that 56% of people still using the app for real-time transit information during COVID-19 identified as female; the pre-COVID-19 gender split was 50-50. The survey also found that people of color made up a greater share of riders during the pandemic: transit ridership dropped by half among white riders, and Black and Latinx riders together became the new majority of the Transit app’s user base. Like the survey’s first finding, people of color who used the app were even more likely to be female: around 70% of Transit’s Black users were female. Ninety-two percent of those surveyed indicated that, during COVID-19, they used transit to commute to work rather than for leisure or to get to school (combined 12%). This finding highlights the importance of continuing transit service throughout tumultuous times, as only 9% of Transit’s users have a car (Transit Blog, 2020). Essential workers rely overwhelmingly on public transit. The data collected by Transit reveals just how valuable public transportation is to public health and safety.

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Accounts of the impacts of COVID-19 on public transit are accumulating. Real-time data, often collected by transit agencies, provides a quantitative glimpse into the world of transit during a global pandemic. Trends and impacts since March are becoming more pronounced as the year progresses, but our ability to make nuanced predictions and projections is still limited. It is clear, though, that public transit agencies will rely heavily on economic relief from the federal government as long as COVID-19 is a significant public health threat. Data on the characteristics of current riders illustrates the necessity of the continuation of federal economic support so that transit agencies can continue to provide safe and reliable service for essential workers. Looking ahead, the long-term impacts of the pandemic are uncertain. Fear of crowded public spaces could impact ridership negatively, even among transit-dependent populations. On the other hand, due to evidence that transit ridership increases during financially-uncertain times, a spike in ridership could be prompted by the pandemic-induced economic downturn.

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, low-income 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 impacts still 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

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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.

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