Tracking the Transit Divide: A Multilevel Modelling Approach of Urban Inequalities and Train Ridership Disparities in Chicago

Using a multilevel modelling approach, this study investigates the impact of urban inequalities on changes to rail ridership across Chicago's "L” stations during the pandemic, the mass vaccination rollout, and the full reopening of the city. Initially believed to have an equal impact, COVID-19 disproportionally impacted the ability of lower socioeconomic status (SES) neighbourhoods' to adhere to non-pharmaceutical interventions: working-from-home and social distancing. We find that "L” stations in predominately Black or African American and Hispanic or Latino neighbourhoods with high industrial land-use recorded the smallest behavioural change. The maintenance of higher public transport use at these stations is likely to have exacerbated existing health inequalities, worsening disparities in users' risk of exposure, infection rates, and mortality rates. This study also finds that the vaccination rollout and city reopening did not significantly increase the number of users at stations in higher vaccinated, higher private vehicle ownership neighbourhoods, even after a year into the pandemic. A better understanding of the spatial and socioeconomic determinants of changes in ridership behaviour is crucial for policymakers in adjusting service routes and frequencies that will sustain reliant neighbourhoods' access to essential services, and to encourage trips at stations which are the most impacted to revert the trend of declining public transport use.

U.S. Public Transportation Ridership Analysis and Prediction based on COVID-19

In this paper, a research was conducted to analyse and predict the impacts of COVID-19 on public transportation ridership in the U.S. and 5 most populous cities of the U.S. (New York City, Los Angeles, Chicago, Houston, Philadelphia). The paper aims to exploit the correlation between COVID-19 and public transportation ridership in the U.S. and make the reasonable prediction by machine learning models, including ARIMA and Prophet, to help the local governments improve the rationality of their policy implementation. After correlation analyses, high level of significant and negative correlations between monthly growth rate of COVID-19 infections and monthly growth rate of public transportation ridership are decidedly validated in the total U.S., and New York City, Los Angeles, Chicago, Philadelphia, except Houston. To analyse the errors of Houston, we consult the literature and made a discussion of Influencing factors. We find that the level of public transportation in quantity and utilization is terribly low in Houston. In addition, the factors, such as the lack of planning law and estimation of urban expressways, the high level of citizens' dependence on private cars and pride of owning cars play a considerable roll in the errors. And the impacts can be predicted to a certain extent through two forecasting models (ARIMA and Prophet), although the precision of our models is not enough to make a precise forecast due to the limitations of model tuning and model design. According to the comparison of the two models, ARIMA models' forecasting accuracy is between 6% and 10%, and Prophet's forecasting accuracy is between 8%-12%, depending on the city. Since the insufficient stationarity, periodicity, seasonality of time series, the Prophet models are hard be more refined. © 2023 SPIE.

Three's a crowd? Examining evolving public transit crowding standards amidst the COVID-19 pandemic

The COVID-19 pandemic dramatically affected public transit systems around the globe. Because transit systems typically move many people closely together on buses and trains, public health guidance demanded that riders should keep a distance of about two meters to others changed the definition of "crowding” on transit in 2020. Accordingly, this research examines how U.S. public transit agencies responded to public health guidance that directly conflicted with their business model. To do this, we examined published crowding standards before the COVID-19 pandemic for a representative sample of 200 transit systems, including whether they started or changed their published standards during the pandemic, as well as the reasons whether agencies publicize such standards at all. We present both descriptive statistics and regression model results to shed light on the factors associated with agency crowding standards. We find that 56% of the agencies surveyed published crowding standards before the pandemic, while only 46% published COVID-19-specific crowding standards. Regression analyses suggest that larger agencies were more likely to publish crowding standards before and during the COVID-19 pandemic, likely because they are more apt to experience crowding. Pandemic-specific crowding standards, by contrast, were associated with a more complex set of factors. We conclude that the relative lack of pandemic standards reflects the uncertainty and fluidity of the public health crisis, inconsistent and at times conflicting with the guidance from public health officials, and, in the U.S., a lack national or transit industry consensus on appropriate crowding standards during the first year of the pandemic.

Investigating changes in longitudinal associations between declining bus ridership, bus service, and neighborhood characteristics

Amid the recent transit ridership decline, gaining an understanding of the factors affecting ridership becomes crucial for transit agencies to utilize limited resources effectively. I use generalized linear multilevel negative binomial models to investigate the longitudinal relationship and changes in the associations between neigh-borhood-level bus ridership and a series of socio-economic and bus service factors in Philadelphia between 2014 and 2018. Data come from passenger boarding at bus stops in Philadelphia. Results show that the associations between bus ridership, population and the number of jobs, and the percent of zero-car households are positive, but weakened over time. The associations between ridership and bus service supply are inelastic. The findings have implications on transit agencies' resource allocation and service adjustments as they recover from the ridership and revenue losses during the COVID-19 pandemic while facing competition from new travel options such as Uber and Lyft.

The impact of Covid-19 pandemic on public transit use: Case study of Konya city

Since March 2019, Turkey has been enforcing various measures on the policies based on the trends in the COVID-19 cases. To restrain the spread of virus, policies limiting the mobility of people (i.e. lock downs, remote working and travel bans) were applied as in many other countries. Furthermore, social distancing calls for health concern directly caused a major reduction in public transit (PT) use. However, economic activities and new normal conditions required return of a part of the commute travels, which brought the issue of use of PT modes. This study focuses on the comparison of the PT mobility during the month of April in the 2019 (pre-pandemic), in 2020 during restrictions and in 2021 under new normal condition using the Smart Card (SC) data in Konya, Turkey. Monthly, daily and hourly distribution of ridership patterns are compared as well as usage patterns and characteristics of different bus lines are examined in detail. The results suggested that during the restrictions, the ridership was about one eight of the pre-pandemic periods, while it increased to 2.5 million ridership in 2021 which is still very low. Daily ridership in 2020 showed no PT mobility due to lockdowns, while during weekdays, hourly ridership distributions were changed parallel to changes in the work/education activity schedules. Evaluation of the bus lines having highest ridership in 2019 with 2020 and 2021 showed some of the bus lines were cancelled during the pandemic and routes/frequencies changed. The results showed the importance of PT management during pandemic which is very challenging due to economic loss and fear of infection by public. However, it should be emphasized the importance of continuation of public transportation in terms of accessibility and equity for all. © 2023 The Authors. Published by ELSEVIER B.V.

Potential of Bike Sharing During Disruptive Public Health Crises: A Review of COVID-19 Impacts

With public transport (PT) continuing to be negatively affected by the coronavirus pandemic and private car usage surging, alternative modes need to be considered. In this study, we review the available evidence (from academic and gray literature sources) on the performance of bike sharing systems (BSSs) during COVID-19 around the world, with the goal of assessing their potential contribution to improving the resilience of transport systems during pandemics and similar disruptive events. We found BSS usage followed a decrease-rebound pattern, with BSSs overall sustaining lower ridership declines and faster recoveries compared with PT. During lockdowns especially, the average duration of BSS trips increased, following a rise in casual users and leisure trips, while commuting trips decreased. Evidence has also been found for a possible modal shift from some PT users to BSSs, with a decline in the share of multimodal trips conducted between PT and BSSs. Bike sharing is perceived as safer than other shared modes (e.g., PT, taxis, and ride-hailing/sharing) but as having a higher infection risk than personal modes (e.g., private car, walking, and personal bike). Moreover, the BSS was an important transport alternative for essential workers, with several operators providing waivers especially to healthcare staff, leading to ridership increases near healthcare facilities and in deprived neighborhoods. Findings from this research support policies for promoting bike sharing, namely through fee reductions, system expansions, and symbiotic integration with PT, as BSSs can increase the sustainability and resilience of transport systems during disruptive public health events like COVID-19.

A multiple mediation analysis to untangle the impacts of COVID-19 on nationwide bus ridership in the United States.

The COVID-19 pandemic has resulted in major consequences for many aspects of human life and the broader economy. Many transportation modes were severely impacted, including public transportation. During the early months of the pandemic in 2020, transit ridership dropped to unprecedented levels. Even by the end of 2022, bus ridership in the United States had not recovered to pre-pandemic levels. Despite the longstanding effects on public transportation, the direct and indirect impacts of the COVID-19 pandemic on bus ridership are largely unknown. In the context of this study, the direct impact refers to a change in travel behavior (i.e., due to the increased spread of COVID-19), while the indirect impact refers to reduced ridership due to factors such as lower employment or increased teleworking. This study proposes a framework to explore the drivers of transit ridership declines during COVID-19. The method is a multiple mediation analysis to estimate the monthly direct and indirect impacts of COVID-19 on bus ridership from March 2020 to December 2021. The results of this study revealed that three mediators (employment, telework, and people relocating) mediated about 13% to 38% of the total decline in bus ridership during the analysis period. The multiple mediation approach used in this study could be applied in many other transportation applications.

Non-Stationary Time Series Model for Station-Based Subway Ridership During COVID-19 Pandemic: Case Study of New York City.

The COVID-19 pandemic in 2020 has caused sudden shocks in transportation systems, specifically the subway ridership patterns in New York City (NYC), U.S. Understanding the temporal pattern of subway ridership through statistical models is crucial during such shocks. However, many existing statistical frameworks may not be a good fit to analyze the ridership data sets during the pandemic, since some of the modeling assumptions might be violated during this time. In this paper, utilizing change point detection procedures, a piecewise stationary time series model is proposed to capture the nonstationary structure of subway ridership. Specifically, the model consists of several independent station based autoregressive integrated moving average (ARIMA) models concatenated together at certain time points. Further, data-driven algorithms are utilized to detect the changes of ridership patterns as well as to estimate the model parameters before and during the COVID-19 pandemic. The data sets of focus are daily ridership of subway stations in NYC for randomly selected stations. Fitting the proposed model to these data sets enhances understanding of ridership changes during external shocks, both in relation to mean (average) changes and the temporal correlations.

Station-Level Effects of the COVID-19 Pandemic on Subway Ridership in the Seoul Metropolitan Area.

This paper investigates the station-level impacts of the coronavirus disease (COVID-19) pandemic on subway ridership in the Seoul Metropolitan Area. Spatial econometric models are constructed to examine the association between ridership reduction caused by the pandemic and station-level characteristics during the pandemic years 2020 and 2021. The results reveal unequal effects on station-level ridership, based on the pandemic waves, the demographics, and the economic features of pedestrian catchment areas. First, the subway system was severely disrupted by the pandemic, with significant decreases in ridership-by about 27% for each of the pandemic years-compared with the pre-pandemic year (2019). Second, the ridership reduction was sensitive to the three waves in 2020 and responded accordingly; however, it became less sensitive to the waves in 2021, indicating that subway usage was less responsive to pandemic waves during the second year of the pandemic. Third, pedestrian catchment areas with higher numbers of younger residents (in their 20s) and older residents (65 years and older), those with more businesses requiring face-to-face interactions with consumers, and stations located in the employment centers were hit the hardest in ridership reduction caused by the pandemic.

Bioprotection of Transportation and Facilities from SARS-CoV-2 (COVID-19).

The recent COVID-19 pandemic has led to a nearly world-wide shelter-in-place strategy. This raises several natural concerns about the safe relaxing of current restrictions. This article focuses on the design and operation of heating ventilation and air conditioning (HVAC) systems in the context of transportation. Do HVAC systems have a role in limiting viral spread? During shelter-in-place, can the HVAC system in a dwelling or a vehicle help limit spread of the virus? After the shelter-in-place strategy ends, can typical workplace and transportation HVAC systems limit spread of the virus? This article directly addresses these and other questions. In addition, it also summarizes simplifying assumptions needed to make meaningful predictions. This article derives new results using transform methods first given in Ginsberg and Bui. These new results describe viral spread through an HVAC system and estimate the aggregate dose of virus inhaled by an uninfected building or vehicle occupant when an infected occupant is present within the same building or vehicle. Central to these results is the derivation of a quantity called the "protection factor"-a term-of-art borrowed from the design of gas masks. Older results that rely on numerical approximations to these differential equations have long been lab validated. This article gives the exact solutions in fixed infrastructure for the first time. These solutions, therefore, retain the same lab validation of the older methods of approximation. Further, these exact solutions yield valuable insights into HVAC systems used in transportation.

Analysis of the impact of the COVID-19 pandemic on the public transport system by bus in Brazil

This article analyzes the impact of the COVID-19 pandemic on public transport by bus in two Brazilian metropolia, Belo Horizonte and Joao Pessoa. Spearman's correlation pointed out a strong relationship between the variation in the number of passengers transported and the restrictive measures to combat the COVID-19 pandemic showing that they probably dictated the use of public transport by the population. However, the correlation between the number of new confirmed cases of COVID-19 and the variation of transported users was weak in Belo Horizonte and insignificant in Joao Pessoa. Given the influence of the stringency measures, the significant differences in correlation values with the variation of passengers were identified and proven, being 60% in Belo Horizonte and 76% in Joao Pessoa. The causality test confirmed that the pandemic intensified the drop in demand for public transport. Therefore, the more severe the policy to combat the transmission of the virus, the greater the relationship with the decrease in demand for buses. Thus, the pandemic was responsible for a significant drop in the number of passengers than the estimated trend for the same period. Finally, results show a crisis in the public transport system by bus in Brazil and the urgent need to rethink strategies to attract users to this service.

Disparities in public transit accessibility and usage by people with mobility disabilities: An evaluation using high-resolution transit data

Many people with mobility disabilities (PwMD) rely on public transit to access crucial resources and maintain social interactions. However, they face higher barriers to accessing and using public transit, leading to disparities between people with and without mobility disabilities. In this paper, we use high-resolution public transit real-time vehicle data, passenger count data, and paratransit usage data from 2018 to 2021 to estimate and compare transit accessibility and usage of people with and without mobility disabilities. We find large disparities in powered and manual wheelchair users' accessibility relative to people without disabilities. The city center has the highest accessibility and ridership, as well as the highest disparities in accessibility. Our scenario analysis illustrates the impacts of sidewalks on accessibility disparities among the different groups. We also find that PwMD using fixed-route service are more sensitive to weather conditions and tend to ride transit in the middle of the day rather than during peak hours. Further, the spatial pattern of bus stop usage by PwMD is different than people without disabilities, suggesting their destination choices can be driven by access concerns. During the COVID-19 pandemic, accessibility disparities increased in 2020, and PwMD disproportionately avoided public transit during 2020 but used it disproportionately more during 2021 compared to riders without disabilities. This paper is the first to examine PwMD's transit experience with large high-resolution datasets and holistic analysis incorporating both accessibility and usage. The results fill in these imperative scientific gaps and provide valuable insights for future transit planning. © 2023 Elsevier Ltd

A method for assessing the COVID-19 infection risk of riding public transit

During the pandemic, to prevent the spread of the virus, countries all adopted various safety measures, including masking, social distancing, and vaccination. However, there is a lack of methods that can quantitively evaluate the effectiveness of these countermeasures. This research first develops a model to quantitively evaluate the infection risk of riding public transit. By utilizing the developed model, the effectiveness of different countermeasures could be evaluated and compared. For demonstration purposes, the developed model is applied to a particular bus route in the City of Houston, Texas. The modeling results show that masking, social distancing, and vaccination can all reduce the infection risk for passengers. And among all these countermeasures, face masking is the most effective one. In addition, model results approve that the COVID-19 infection risk is highly related to the exposure time and the risk can be controlled by reducing the exposure time. Thus, a new strategy named the "split route strategy” is proposed and compared with the "capacity reduction strategy” using the model developed. In addition, a cost-benefit analysis is performed to assess the feasibility of the proposed "split route strategy”. Furthermore, two interviews were conducted with practitioners at Houston Metro. Both interviewees believe that face masking could significantly prevent the spread of the virus, which validated the model results. © 2022

Evaluation of the short- and long-term impacts of the COVID-19 pandemic on bus ridership in Miyazaki City, Japan

We used a Bayesian structural time series (BSTS) model to evaluate the short- and long-term impacts of the coronavirus disease 2019 (COVID-19) pandemic on transit ridership. We accessed smart-card data from Miyazaki City, Japan. We defined attributes based on card types (commuters, students and elders) and aggregated attributes (high-frequency users and "frequently used bus-stop pairs”) and analyzed the differences between all users and the extracted groups. Among card types, the short-term impact on elders was almost identical to that of all users, however, the short-term impact of the pandemic on commuters was much smaller and that of students was much larger than that of all users. The long-term trend of commuters was less fluctuated than that of all users. The long-term ridership recovery of students was higher than that of all users. Among aggregated attributes, the short-term impact was smaller on "high-frequency users” than on all users: the decrease in ridership immediately after the appearance of COVID-19 was smaller among "high-frequency users” than among all users. The long-term recoveries in the riderships of the extracted subsets were slower than the recoveries of riderships of all users. © 2023 The Authors

The effects of COVID-19 epidemic on public transport ridership and frequencies. A case study from Tampere, Finland.

The COVID-19 epidemic has created unforeseen effects in public transport in cities around the world, including Tampere, where a 70% decrease in the number of passengers occurred in spring 2020. The purpose of this paper is to study changes in public transport ridership, frequencies, and average fill rates during the epidemic in May 2020 compared to normal circumstances in January 2020 using map-based analysis. We used data provided by Tampere regional transport to create the public transport network and to assess the frequencies, ridership and average fill rates in different areas of Tampere. The paper presents the method used to analyze how the modified frequencies meet the decreased demand of public transport in Tampere. These results indicate that the decrease in ridership was great in almost all areas, except some eastern parts of Tampere. The frequencies were decreased in all areas but kept at a sufficient level. When analyzing fill rates, we found that the bus lines coming from east of Tampere were more crowded on average during the COVID-19 epidemic in May compared to January. In other areas, fill rates were lower. The results suggest that in Tampere, frequencies were mostly managed to maintain at a sufficient service level. However, the analyses also reveal that frequencies were not adjusted successfully in all areas with high fill rates in some routes. It is important to notice, that the public transport planners were facing a deviant situation with COVID-19 and that in future, there will be more information to help decision-making.

Requiem for transit ridership? An examination of who abandoned, who will return, and who will ride more with mobility as a service

To adhere to health regulations and reduce the risks associated with the COVID-19 pandemic, employers, mobility operators, and travelers alike adopted new strategies such as teleworking, rigorous sanitation, and social distancing. In this research, we examine the individual-level factors contributing to transit ridership abandonment and return decisions. We utilize comprehensive survey-based data of transit users in the Chicago metropolitan area (N = 5648) collected prior to reopening. We investigate three ridership behaviors, namely (1) discontinued public transit ridership, (2) the intent to return to pre-pandemic transit ridership levels once health concerns are alleviated, and (3) the likelihood of using public transit more often if its fare systems are integrated with other mobility services such as ridehailing and micromobility. Examining the role of sociodemographics, employment characteristics, transit investment priorities, and travel behavior before and during the pandemic, this research reveals fine-grained details about transit usage decline, as well as future intentions. The results indicate that teleworking, unemployment, and vehicle access are the major factors behind discontinued transit ridership. Analysis of race, ethnicity, and gender effects reveals that vulnerable users often have a higher risk of abandonment coupled with a lower likelihood of returning. These results point to the need for transit agencies to consider the specific concerns of ethnic/racial minorities and women. Encouragingly, there is an opportunity for agencies to attract more ridership with fare integration. Several respondent segments would use transit more if fare systems are integrated with ridehailing and micromobility, highlighting the importance of lowering the barriers to accessing these mobility services. This research informs several policies that can be adopted by transit agencies and other mobility providers. We discuss the importance of an equitable return to transit, possibilities for Mobility-as-a-Service with fare integration as a starting point and stress the significance of teleworking in future transit policies.

The impact of COVID-19 on route-level changes in transit demand an analysis of five transit agencies in Florida, USA

This study examined the impacts of COVID-19 on changes in route-level transit demand across five transit agencies in the state of Florida. Data for 120 routes from five transit agencies were used to develop two-stage instrumental variable models. Data from January of 2019 to December of 2020 were used in the analysis. Routes that served a greater mix of land-uses experienced a smaller decline in ridership. The impacts of several other land-use variables were, however, not consistent across the five transit agencies. Fare suspension was estimated to have a positive impact on ridership. In contrast, occupancy reduction measures (to promote social distancing within the transit vehicle) had a very strong negative impact on demand. The magnitude of the negative impact of occupancy reduction was larger than the positive impacts of fare suspension. Extending this analysis to a larger set of routes across more agencies would be useful in enhancing the robustness of the findings from our models. Extending our analysis to include data from 2021 and later to capture the recovery phase is also an important direction for future work. © 2022

The impact of COVID-19 and related containment measures on Bangkok's public transport ridership.

The COVID-19 pandemic and related measures used to contain its spread affected public transport ridership in cities around the world. In Thailand, the government issued 41 Royal Decrees between April 2020 and December 2021 to mitigate the spread of the pandemic. In this study, we investigate how Bangkok's public transport services (bus, metro, and boat) have been affected during this period by analyzing the daily ridership data, confirmed COVID-19 cases, and aggregated travel trends by trip destinations using from Google mobility reports. The results show that public transport ridership decreased as daily COVID cases increased and the levels of restraining measures became higher. However, other factors, such as relative strictness compared to earlier measures and sequencing of the measures seems to have had an impact on the ridership. Moreover, the impact on ridership trends is unique for each of the three modes. Bus and metro ridership appear to be more sensitive to the changes in restrictions than the boats. Bus and metro ridership also shows similar changes in the travel trends concerning the place of visit. The findings reported here provide first insights into how Bangkok's public transport systems were affected and suggest the rationale of why different public transport modes were affected differently. These results can be useful for researchers and for decision-makers who plan and design policies and measures for public transport services.

Impacts of COVID-19 on Bus Ridership and Recovery Trends in Syracuse, New York

COVID-19 had serious repercussions on public transportation throughout the USA. The aftermath of the peak of the crisis marked the path towards a slow and gradual recovery characterizing the shift to a new normal. Given the limited information on the recovery trends of public transportation, this paper compares the actual ridership and bus supply data for the years of 2019 and 2020 to study the timeline impacts of the pandemic on the bus system of the mid-sized city of Syracuse, NY. A data-driven analysis is presented across the city's bus routes, university bus routes, and categorical bus stops. Various census tract socio-demographic data are also correlated with passenger activity changes and mapped using ArcGIS. The findings show that overall bus ridership in 2020 fell by 70%, on average, during the three months that followed the onset of the pandemic. Since the lifting of the initial restrictions, concerns about using public transportation had partially been alleviated;however, passengers remained reluctant with ridership decline stabilizing at approximately 55% during the last four months of the year. While bus lines serving the university area, which houses a high percentage of youth, were severely affected by the pandemic, passenger activity near hospital stops were less affected and those near major supermarkets/ hypermarkets seemed unaffected, showing a surge especially in the two months that followed the onset of the pandemic. Passenger activity at census tracts having low poverty levels mostly located on the outskirts of the city of Syracuse were the least affected tracts in the last six months of 2020. It is anticipated that the insights presented will help service planners in preparing for similar future events by better understanding what stops and routes are deemed essential during a public health crisis and how the socio-demographics impacted the recovery after restrictions were removed. [ FROM AUTHOR]

Vehicle access and falling transit ridership: evidence from Southern California.

We examine pre-COVID declines in transit ridership, using Southern California as a case study. We first illustrate Southern California's unique position in the transit landscape: it is a large transit market that demographically resembles a small one. We then draw on administrative data, travel diaries, rider surveys, accessibility indices, and Census microdata for Southern California, and demonstrate a strong association between rising private vehicle access, particularly among the populations most likely to ride transit, and falling transit use. Because we cannot control quantitatively for the endogeneity between vehicle acquisition and transit use, our results are not causal. Nevertheless, the results strongly suggest that increasing private vehicle access helped depress transit ridership. Given Southern California's similarity to most US transit markets, we conclude that vehicle access may have played a role in transit losses across the US since 2000.