Analyzing the Nexus of Virus Outbreaks, Public Policy, and Ridership

Michael Dillman and Christine Posvistak

The novel coronavirus changed collective societal behaviors. From wearing masks to stay-at-home mandates, how people conduct their lives changed dramatically. Working from home became a luxury (Valentino-DeVries, Lu, & Dance, 2020) and, for those who cannot work from home, transportation became an even more important consideration. As much as ever, public transportation is critical for many people to get to work, buy groceries, go shopping, go to school, and access education (Jansuwan, Christensen, & Chen, 2013).

In this empirical study, we seek to understand whether or not the novel coronavirus changed how people use public transportation along the Wasatch Front in Utah. Our approach is empirical, as we assess the relationship between public transportation ridership and coronavirus prevalence through Utah Transit Authority and Utah Department of Health data. We further draw on a critical lens to discuss the impact of the COVID-19 crisis on marginalized communities. This study is of interest to public administrators because of its potential implications for policy formulation and implementation, especially as it pertains to public transportation during a public health crisis.

Literature Review

 COVID-19 spread globally from its outbreak in China at the beginning of 2020, negatively impacting industries and economies globally.  The rapid spread of the COVID-19 virus has been attributed to the globalization and hypermobility of our current lifestyle (Tirachini & Cats, 2020). Pandemics are a public health problem, but also socio-economic and political concerns (Chakraborty & Maity, 2020). Nearly nine out of ten American adults have said that the coronavirus outbreak impacts their personal lives, and 44% of those adults say that their life has changed in a major way (Pew Research Center, 2020). People have changed their behavior by reducing their out-of-home activities by more than 50% during the pandemic (Fatmi,  2020). Despite the fact that the highest daily frequency of travel is found for routine shopping and work-related travel (Fatmi, 2020), Utah’s public transit ridership trends are lower than normal and it is speculated that this is because of fear of exposure to the virus (Reed, 2020).

Before the pandemic, the motivation behind taking public transportation was thought to be two-fold: self-interest and eco-friendliness. Hoang-Tung, Kojima, and Kubota (2015) report that while both are important, self-interest is the driving concern. This distinction is important, especially in the face of a pandemic; those who have the choice not to take public transportation may decide not to, based on their own self-interest.

Different types of transit attract people of different socio-economic statuses. Research suggests that those who take rail tend to be similar in wealth to private vehicle travelers, while bus patrons have far lower incomes, with the gap growing over time (Taylor & Morris, 2014). A local study that interviewed 218 Northern Utah public transit users shows similar trends, as about two in three (68%) respondents earned less than $15,000 per year (Jansuwan, 2013). Perhaps unsurprisingly, as the coronavirus has spread, differences in mobility adaptations have emerged between people of different resource levels: people in the top decile of wealth reduced their mobility up to twice as much as those in the bottom decile (Fraiberger, et. al., 2020).  Lower-income individuals have less access to flexible transportation modes (e.g. automobiles), putting them at a disadvantage when public transportation availability is reduced. Additionally, limited transportation options isolate the poor from government services and programs designed to lift them out of poverty (Jansuwan, Christensen, & Chen, 2013). When heading to work, a grocery store, or to school,  many lower-income individuals simply do not have a choice between whether or not to use public transportation.

These facts notwithstanding, national public transit trends show notable ridership declines as people choose to stay home or use other transportation methods. The COVID-19 pandemic’s onset saw a large loss in ridership and fare revenue in many metropolitan areas. Multiple examples exist, such as an 88% loss of ridership for New Jersey Transit, a loss of 60% of subway ridership in New York’s Metropolitan Transportation Authority, a 60% loss for Denver’s Regional Transportation District, and a 90% loss for the Bay Area Rapid Transit in San Francisco (Mallet, 2020). In this study, we seek to understand how public transportation ridership in and around Salt Lake City, Utah, has been impacted during the COVID-19 pandemic.

Research Design

 We use secondary data in an observational research design to examine how the COVID-19 pandemic may have impacted public transit ridership in Utah. We used Utah Transit Authority ridership data from 2016-2020 and COVID-19 positivity data from the Utah Department of Health.  Additionally, by choosing five critical policy decisions relating to COVID-19 (such as state of emergency orders and county-level phasing restrictions) and comparing the average ridership the week prior to and after the policy change, we examine the relationship between ridership and COVID-19 policies. Although the data do not allow us to examine which populations’ ridership has been most affected by the downturn, we separate ridership data by transit mode to make inferences to which riders the pandemic most impacted. We summarized our descriptive analysis findings in a series of figures and tables in the following section.


This study aimed to understand how the COVID-19 pandemic crisis and policy responses may have impacted how people use public transportation along Utah’s Wasatch Front. We examine the question by first comparing ridership numbers (by mode) between 2019-2020. We then examine the association between ridership changes and COVID-19 data. Finally, we examine differences in ridership as they correspond to state and county COVID-19 policy responses.

Before presenting the findings of the aforementioned analyses, we present descriptive data of how Utah Transit Authority ridership has changed since the beginning of the year. Figure 1 shows this ridership by mode. All bus ridership data is presented in aggregate, alongside FrontRunner (a commuter train which connects Salt Lake City with surrounding municipalities) and TRAX (a light rail system primarily within Salt Lake City). As seen in the figure, there is a massive downturn in ridership across all modes beginning in March, which begins a slow but steady recovery from May through September.


Figure 1. 2020 UTA Ridership by Mode


Figure 2 shows the same data, but compares it with 2019 ridership data by mode. The figure illustrates a likely annual trend of ridership dipping in summer months and an incline in ridership towards the end of the summer and early fall. It is worth noting that, while not depicted, the trend holds up across all four years of ridership data that we analyzed.


Figure 2.  UTA Ridership by Mode, 2019 and 2020


We next move from illustrative figures to basic descriptive analyses of comparative ridership data. The findings in Table 1 present the scale of the 2020 drop in ridership by mode in comparison with the prior year. For example, FrontRunner ridership bottomed out at just 5% of last year’s ridership in April, while Bus and TRAX ridership went down to 7% and 8% of 2019 ridership, respectively. Not only did bus ridership not dip as low as FrontRunner, but it also seems to be recovering at the fastest rate. By July, bus ridership had bounced back to 21% of the prior year’s ridership, while FrontRunner recovered to just 14% over the same period.


Table 1. Percent change in UTA ridership in 2020 from the same month in 2019, by Mode
Buses FrontRunner
(commuter rail)
(light rail)
January  +8% +3% -6%
February +13% -1% -8%
March -46% -54% -54%
April -92% -95% -93%
May -86% -91% -85%
June -82% -89% -83%
July -79% -86% -79%
August -78% -86% -81%
September -78% -85% -81%


To examine a possible relationship between COVID-19 prevalence and UTA ridership, we plot the two on a dual axis graph, as displayed in Figure 3. After the initial dip at the onset of COVID-19 in March, ridership slowly increased throughout the year, with a notable spike beginning in August. The trend in COVID-19, in contrast, shows a “wave” of positive cases in June and July, followed by a possible second wave (comprised of multiple spikes) which began in early September.


 Figure 3. Utah COVID-19 Cases and 2020 UTAH Ridership


To examine the relationship further, we ran Pearson’s R correlation test, as a measure of association. The correlation coefficient between COVID-19 data and ridership numbers was 0.3 and 0.7, depending on whether positivity rate versus or case count (respectively) was used in the analysis. The findings indicates a moderate-to-strong, positive bi-variate relationship between COVID-19 testing/incidence and Utah Transit Authority ridership (Johnson, 2015). Importantly, we suspect the relationship between COVID-19 incidence and ridership is more complicated than the findings seems to indicate, as at least one “third variable”—time—likely accounts for much of the positive relationship(s), as discussed further below.

Finally, Table 2 shows the relationship between state and county COVID-19 related policy changes and Utah Transit Authority ridership. The findings indicate there may be an association between policies and ridership changes in the first three months of the COVID-19 crisis. As one might expect, the first two shutdown policies correspond with declines in ridership and a phasing out of restrictions corresponds with an increase in ridership. However, a state reissuance of a state of emergency in August is (counterintuitively) followed by a surge in ridership, and a drop in ridership occurred after Salt Lake County downgraded the COVID-19 threat level in September. Both of these latter policy changes had the opposite relationship with ridership that the first three policy changes exhibited.


 Table 2. Change in average ridership in the weeks before and after policy changes
Policy Date Jurisdiction Change in Average Ridership Week Before/After
State of emergency declared 6-Mar State -4158 (-56%)
Stay-at-home order declared 27-Mar State -375 (-48%)
Restrictions begin phasing out for most businesses 1-May State and county 63 (+20%)
State of emergency reissued 21-Aug State 795 (+74%)
Salt Lake County moves to from orange to yellow 4-Sep County -165 (-10%)



COVID-19 dramatically altered the way that people across the globe are conducting their lives and such lifestyle habit changes seem to be reflected in the number of people riding public transportation across municipalities, the country, and the globe. With the onset of the Coronavirus, there were dramatic drops public transportation ridership numbers internationally (Mallet, 2020) – and we show a similar trend in Utah. Corresponding with the onset of Utah COVID-19 cases in March, Utah Transit Authority ridership decreased markedly. While Utah generally sees a downward trend of ridership in the summer – which we postulate is due to school attendance – the March 2020 fall in ridership is both earlier, sharper, and more prolonged than in prior years.

Data from prior years indicates that Utah Transit Authority ridership tends to exhibit an upswing around the end of summer or beginning of fall. We expected this not to be the case in 2020, as the timeframe corresponds with a dramatic increase in COVID-19 cases. Our expectation was wrong, and instead our findings indicate a positive correlation between COVID-19 incidence data and late-summer Utah Transit Authority ridership. We suspect the unanticipated finding may be partially explained by the usual uptick in ridership exhibited when school starts along the Wasatch Front. A second, perhaps complimentary, explanation occurs to us – it could be that unemployment services are a possible cause of increased ridership. More specifically, the federal Coronavirus Aid, Relief, and Economic Security (CARES) Act’s expanded unemployment benefits ended around the same time that we see a 2020 ridership increase. It is possible the end of expanded benefits forced some people back out to public transit for the purposes of commuting to and from their workplace (Smith, 2020).

Our findings shed some light on the relationship between state and local COVID-19 mandates and Utah Transit Authority ridership. We were surprised to find that while ridership went down with the initial three months of policies (as one might expect), there was a (perhaps counterintuitive) surge of ridership despite a state of emergency issued in August. Similarly, we witness a (perhaps counterintuitive) decrease in ridership after Salt Lake County downgraded their threat level. Collectively, the findings seem to indicate that other factors – such as school operations, economic factors, and factors associated with the passage of time (e.g. “lockdown fatigue”) – likely hold greater sway over Utah Transit Authority ridership than state and county COVID-19 policies.

Finally, when considering ridership changes across different public transportation modes, we find ourselves referring back to Taylor and Morris’s (2014) study regarding who rides what types of public transportation. Their study (if generalized) would suggest that, people who ride the FrontRunner (the commuter train) are likely wealthier than other transit users. This might help explain why FrontRunner ridership numbers have not recovered at the same rate as those of Utah Transit Authority buses or TRAX (the light rail). It is possible that people who rely on buses or the TRAX system have (1) either had to return to work at a greater rate than FrontRunner riders, or (2) lack alternative to public transit that are available to FrontRunner riders – most notably, private vehicles.

A limitation of this study is that we cannot say definitively what factors lead to the ridership increase. Another limitation of this study is that the dataset we used does not include ridership by stop so we are unable to tell which places saw the steepest declines in ridership.


Our aim in this study was to understand how peoples’ use of public transportation along Utah’s Wasatch Front has changed during the novel coronavirus pandemic. Our descriptive analysis indicated a steep downturn in ridership in the early months of the crisis and that the decline in ridership differed by mode of transportation. We used a combination of Utah Transit Authority data on ridership and Utah Department of Health data on COVID-19 cases to explore the relationship between COVID-19 prevalence and ridership changes. We found that ridership recovered a portion of the losses in ridership over time, despite rising COVID-19 cases and test positivity rates.

We further found that state and county policy responses to COVID-19 likely had little-to-no effect on Utah Transit Authority ridership – especially after the first few months of the pandemic. If anything, during the late summer and fall, they may have even had the opposite effect of what was intended. For example, after Governor Herbert reissued a State of Emergency in August, but public transit ridership increased by 74% the following week. We posited two possible reasons for the increase: some people may have had to return to work (possibly due to the end of CARES Act benefits), but perhaps more notable is that the increase coincides with a standard ridership uptick as school starts along the Wasatch Front. Finally, we highlighted that bus ridership didn’t bottom out as hard at the onset of the pandemic – and recovered quicker – than FrontRunner commuter train ridership. The findings aligns with existing literature, which suggests FrontRunner riders are more likely wealthier, white-collar workers (Taylor & Morris, 2014).

We believe that our findings could help public administrators understand the downturn in ridership and the relationships between public health indicators in a crisis, public policy responses, and public transportation demand. Future avenues of research could help shed more light on which areas along the Wasatch Front have been most impacted, when, and why. By using ridership data that includes data by stop or region, new research could examine in greater depth for whom ridership has changed the most since the onset of the COVID-19 crisis.


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Inquiry of the Public Sort Copyright © 2020 by Michael Dillman and Christine Posvistak is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, except where otherwise noted.

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