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1. Impact of COVID-19 on Public Transit Accessibility and Ridership

   Wilbur, Michael ; Ayman, Afiya ; Ouyang, Anna ; Kabir, Riyan ; Vadali, Abhiram ; Pugliese, Philip ; Freudberg, Dan ; Laszka, Aron ; Dubey, Abhishek ( January 2021 , Publications Transportation Research Board)

   null (Ed.)

   Public transit is central to cultivating equitable communities. Meanwhile, the novel coronavirus disease COVID-19 and associated social restrictions has radically transformed ridership behavior in urban areas. Perhaps the most concerning aspect of the COVID-19 pandemic is that low-income and historically marginalized groups are not only the most susceptible to economic shifts but are also most reliant on public transportation. As revenue decreases, transit agencies are tasked with providing adequate public transportation services in an increasingly hostile economic environment. Transit agencies therefore have two primary concerns. First, how has COVID-19 impacted ridership and what is the new post-COVID normal? Second, how has ridership varied spatio-temporally and between socio-economic groups? In this work we provide a data-driven analysis of COVID-19’s affect on public transit operations and identify temporal variation in ridership change. We then combine spatial distributions of ridership decline with local economic data to identify variation between socio-economic groups. We find that in Nashville and Chattanooga, TN, fixed-line bus ridership dropped by 66.9% and 65.1% from 2019 baselines before stabilizing at 48.4% and 42.8% declines respectively. The largest declines were during morning and evening commute time. Additionally, there was a significant difference in ridership decline between the highest-income areas and lowest-income areas (77% vs 58%) in Nashville. 

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2. Impact of COVID-19 on Public Transit Accessibility and Ridership

   https://doi.org/10.1177/03611981231160531  

   Wilbur, Michael ; Ayman, Afiya ; Sivagnanam, Amutheezan ; Ouyang, Anna ; Poon, Vincent ; Kabir, Riyan ; Vadali, Abhiram ; Pugliese, Philip ; Freudberg, Daniel ; Laszka, Aron ; et al ( April 2023 , Transportation Research Record: Journal of the Transportation Research Board)

   COVID-19 has radically transformed urban travel behavior throughout the world. Agencies have had to provide adequate service while navigating a rapidly changing environment with reduced revenue. As COVID-19-related restrictions are lifted, transit agencies are concerned about their ability to adapt to changes in ridership behavior and public transit usage. To aid their becoming more adaptive to sudden or persistent shifts in ridership, we addressed three questions: To what degree has COVID-19 affected fixed-line public transit ridership and what is the relationship between reduced demand and -vehicle trips? How has COVID-19 changed ridership patterns and are they expected to persist after restrictions are lifted? Are there disparities in ridership changes across socioeconomic groups and mobility-impaired riders? Focusing on Nashville and Chattanooga, TN, ridership demand and vehicle trips were compared with anonymized mobile location data to study the relationship between mobility patterns and transit usage. Correlation analysis and multiple linear regression were used to investigate the relationship between socioeconomic indicators and changes in transit ridership, and an analysis of changes in paratransit demand before and during COVID-19. Ridership initially dropped by 66% and 65% over the first month of the pandemic for Nashville and Chattanooga, respectively. Cellular mobility patterns in Chattanooga indicated that foot traffic recovered to a greater degree than transit ridership between mid-April and the last week in June, 2020. Education-level had a statistically significant impact on changes in fixed-line bus transit, and the distribution of changes in demand for paratransit services were similar to those of fixed-line bus transit. 

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3. Evolution of Mode Use During the COVID-19 Pandemic in the United States: Implications for the Future of Transit

   https://doi.org/10.1177/03611981231166942  

   Magassy, Tassio B. ; Batur, Irfan ; Mondal, Aupal ; Asmussen, Katherine E. ; Bhat, Chandra R. ; Salon, Deborah ; Bhagat-Conway, Matthew ; Javadinasr, Mohammadjavad ; Chauhan, Rishabh ; Mohammadian, Abolfazl ; et al ( May 2023 , Transportation Research Record: Journal of the Transportation Research Board)

   The COVID-19 pandemic has brought about transformative changes in human activity-travel patterns. These lifestyle changes were naturally accompanied by and associated with changes in transportation mode use and work modalities. In the United States, most transit agencies are still grappling with lower ridership levels, thus signifying the onset of a new normal for the future of transit. This paper addresses this challenge using a novel panel survey data set collected from a representative sample of individuals across the United States. The study involved the estimation of a panel multinomial probit model of mode choice to capture both socio-economic effects and period (pre-, during-, and post-COVID) effects that contribute to changes in mode choice. This paper provides rich insights into the evolution of commute mode use as a result of the pandemic, with a particular focus on public transit. Through a rigorous modeling approach, this paper provides a deep understanding of how transit use has evolved, how it is likely to evolve into the future, and the socio-economic and demographic characteristics that affect the evolution (and expected future use) of public transit. Results suggest that transit patronage is likely to remain depressed by about 30% for the foreseeable future, in the absence of substantial changes in service configurations. This study also shows that minority groups and those living in higher density regions are more likely to exhibit a return to transit use in the post-pandemic period. 

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4. Exploring the interaction effect of poverty concentration and transit service on highway traffic during the COVID-19 lockdown

   https://doi.org/10.5198/jtlu.2021.1978  

   Tao, Tao ; Cao, Jason ( January 2021 , Journal of Transport and Land Use)

   During COVID-19 lockdowns, transit agencies need to respond to the decline in travel but also maintain the essential mobility of transit-dependent people. However, there are a few lessons that scholars and practitioners can learn from. Using highway traffic data in the Twin Cities, this study applies a generalized additive model to explore the relationships among the share of low-income population, transit service, and highway traffic during the week that occurred right after the 2020 stay-at-home order. Our results substantiate that transportation impacts are spread unevenly across different income groups and low-income people are less able to reduce travel, leading to equity concerns. Moreover, transit supply influences highway traffic differently in areas with different shares of low-income people. Our study suggests that transportation agencies should provide more affordable travel options for areas with concentrated poverty during lockdowns. In addition, transit agencies should manage transit supply strategically depending on the share of low-income people to better meet people’s mobility needs. 

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5. Predicting Public Transportation Load to Estimate the Probability of Social Distancing Violations

   Martinez, Juan ; Mukhopadhyay, Ayan ; Ayman, Afiya ; Wilbur, Michael ; Pugliese, Philip ; Freudberg, Dan ; Laszka, Aron ; Dubey, Abhishek ( January 2021 , Proceedings of the Workshop on AI for Urban Mobility at the 35th AAAI Conference on Artificial Intelligence)

   Public transit agencies struggle to maintain transit accessibility with reduced resources, unreliable ridership data, reduced vehicle capacities due to social distancing, and reduced services due to driver unavailability. In collaboration with transit agencies from two large metropolitan areas in the USA, we are designing novel approaches for addressing the afore-mentioned challenges by collecting accurate real-time ridership data, providing guidance to commuters, and performing operational optimization for public transit. We estimate rider-ship data using historical automated passenger counting data, conditional on a set of relevant determinants. Accurate ridership forecasting is essential to optimize the public transit schedule, which is necessary to improve current fixed lines with on-demand transit. Also, passenger crowding has been a problem for public transportation since it deteriorates passengers’ wellbeing and satisfaction. During the COVID-19 pandemic, passenger crowding has gained importance since it represents a risk for social distancing violations. Therefore, we are creating optimization models to ensure that social distancing norms can be adequately followed while ensuring that the total demand for transit is met. We will then use accurate forecasts for operational optimization that includes (a) proactive fixed-line schedule optimization based on predicted demand, (b) dispatch of on-demand micro-transit, prioritizing at-risk populations, and (c) allocation of vehicles to transit and cargo trips, considering exigent vehicle maintenance requirements (i.e., disinfection). Finally, this paper presents some initial results from our project regarding the estimation of ridership in public transit. 

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