Search

The COVID-19 pandemic has had a significant impact on public transit ridership in the United States, especially for rail transit. Land use, development density, and the pedestrian environment are strongly associated with station-level transit ridership. This study examines how these characteristics affect transit ridership pre- and post-COVID and how they differ across station types based on longitudinal data for 242 rail stations belonging to Bay Area Rapid Transit, San Diego Metropolitan Transit System, Sacramento Regional Transit, and LA Metro between 2019 and 2021. We found overall a 72% decrease in station-level ridership, but changes were not uniform. Station areas with a higher number of low-income workers and more retail or entertainment jobs tend to have lower ridership declines, while areas with a large number of high-income workers, high-wage jobs, and higher job accessibility by transit had more ridership losses. When comparing station area ridership and activity changes based on mobile phone user data, ridership declined more drastically than activity across all four rail systems, which implies that rail transit riders switched to other modes of transportation when accessing the station areas. Given these findings, it is likely that rail transit services oriented toward commute travel, especially core station areas with jobs for higher income workers, will continue to have an uneven recovery, posing critical implications for transit resilience planning and equity in the post-pandemic era. Considering sources of funding other than passenger fares to sustain rail transit, strategizing to reinvent and reinforce downtowns as destinations, and shifting rail transit services to appeal to non-commute travel can be promising strategies to support rail transit.

Peaking on public transit—the concentration of ridership in peak times and directions into and out of central areas—has waxed in the U.S. over the past century, as public transit has lost more mode share at off-peak times, in off-peak directions, and among non-commute trips. A notable pre-pandemic manifestation of this chronic problem was on Bay Area Rapid Transit, the San Francisco Bay Area’s regional heavy rail system. While BART staved off an absolute ridership decline longer than most American transit operators in the mid- and late-2010s, it did so almost entirely due to peak gains in riders offsetting off-peak losses. As a result, the system experienced worsening passenger crowding at some times and places, expanding underutilization of capacity at many others, and the prospect of enormous expenditures to accommodate rising transbay passenger demand. To examine the factors driving transit use in the 2010s, we model peak and off-peak BART trips as a function of station area and system characteristics. We uniquely use origin–destination pairs as the unit of analysis in order to separately measure influences at both ends of the trip. We find that transfers and travel time most influence peak and off-peak BART ridership and that station-area employment and time-competitiveness with driving particularly influence peak patronage. Over time in our models, the associations between ridership and transit travel time weakened, while the associations between ridership and transfers, employment, and time-competitiveness with driving grew stronger. In sum, we find that the peaking problem plaguing public transit systems for decades worsened in the years leading up to the pandemic—on this one nationally significant U.S. transit system, at least—which poses potentially substantial financial challenges in the years ahead.

A bikeshare system offers a convenient and cost-effective transportation service, providing shared bicycles for short-term use by individuals. It promotes affordability for users while fostering a healthier environment. By offering an alternative for those without access to private vehicles, it helps mitigate the rise in private car usage. Bike sharing also provides an important first-mile/last-mile commuting option. This study focuses on investigating the effects of the COVID-19 pandemic outbreak on bikeshare ridership, with a specific case study centered around Houston, Texas. The employed methodology involves a descriptive analysis and Negative Binomial regression modeling to uncover the relationship between the dependent variable (ridership) and the independent variables. The descriptive analysis revealed an overall increase in ridership during the COVID-19 period in 2020. Notably, longer duration trips were substantially higher in 2020 compared to 2019. Furthermore, the majority of trips occurred during off-peak hours, followed by evening and morning peak periods. Through regression analysis, this study found that the COVID-19 pandemic had a statistically significant positive impact on average daily ridership, with the number of COVID-19 cases positively influencing ridership levels. Additionally, the weekend indicator had a statistically significant positive impact on the average daily ridership. On the other hand, the temperature indicator did not show any significant impact on the average daily ridership, while precipitation had a statistically significant negative impact, leading to decreased ridership levels. The study highlights the significance of various factors in influencing bikeshare usage, contributing to a better understanding of urban transportation dynamics during such unprecedented times.

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.

Although the COVID-19 pandemic highly impacted transit ridership as people reduced or stopped travel, these changes occurred at different rates in different regions across the United States. This study explores the impacts of COVID-19 on ridership and recovery trends for all federally funded transit agencies in the United States from January 2020 to June 2022. The findings of this analysis show that overall transit ridership hit a 100-year low in 2020. Changepoint analysis revealed that June 2021 marked the beginning of the recovery for transit ridership in the United States. However, even by June 2022, rail and bus ridership were only about two-thirds of the pre-pandemic levels in most metropolitan statistical areas (MSAs). Only in a handful of MSAs like Tampa and Tucson did rail ridership reach or exceed 2019 ridership. This retrospective study concludes with a discussion of some longer-term changes likely to continue to impact ridership, such as increased telecommuting and operator shortages, as well as some opportunities, such as free fares and increased availability of bus lanes. The findings of this study can help inform agencies about their performance compared to their peers and highlight general challenges facing the transit industry.

Public investment in transit increased following the Great Recession, yet transit use nationally mostly fell, even prior to the 2020 pandemic. We investigate this troubling disjuncture by comparing transit ridership trends during the 2010s in two of America’s largest regions: Greater Los Angeles and the San Francisco Bay Area. While both California regions lost transit riders, we see substantial differences in the scale, timing, geography, and modes of these declines. In the LA area, ridership fell longer and further, spread more across routes, times, and sub-regions and concentrated on the region’s dominant operator. In both regions, increasing auto access appears to have played a central role, albeit in different ways. Greater LA saw increased automobile ownership, particularly among high-propensity transit riders. In the Bay Area, as jobs and housing have dispersed, ridehail services like Lyft and Uber may have eroded non-commute transit use.

A bikeshare system offers a convenient and cost-effective transportation service, providing shared bicycles for short-term use by individuals. It promotes affordability for users while fostering a healthier environment. By offering an alternative for those without access to private vehicles, it helps mitigate the rise in private car usage. Bike sharing also provides an important first-mile/last-mile commuting option. This study focuses on investigating the effects of the COVID-19 pandemic outbreak on bikeshare ridership, with a specific case study centered around Houston, Texas. The employed methodology involves a descriptive analysis and Negative Binomial regression modeling to uncover the relationship between the dependent variable (ridership) and the independent variables. The descriptive analysis revealed an overall increase in ridership during the COVID-19 period in 2020. Notably, longer duration trips were substantially higher in 2020 compared to 2019. Furthermore, the majority of trips occurred during off-peak hours, followed by evening and morning peak periods. Through regression analysis, this study found that the COVID-19 pandemic had a statistically significant positive impact on average daily ridership, with the number of COVID-19 cases positively influencing ridership levels. Additionally, the weekend indicator had a statistically significant positive impact on the average daily ridership. On the other hand, the temperature indicator did not show any significant impact on the average daily ridership, while precipitation had a statistically significant negative impact, leading to decreased ridership levels. The study highlights the significance of various factors in influencing bikeshare usage, contributing to a better understanding of urban transportation dynamics during such unprecedented times. [ABSTRACT FROM AUTHOR]

Many transit providers changed their schedules and route configurations during the COVID-19 pandemic, providing more frequent bus service on major routes and curtailing other routes, to reduce the risk of COVID-19 exposure. This research first assessed the changes in MARTA service configurations by reviewing the pre-pandemic vs. during-pandemic General Transit Feed Specification (GTFS) files. Energy use per route for a typical week was calculated for pre-pandemic, during-closure, and post-closure periods by integrating GTFS data with MOVES-Matrix transit energy and emission rates. MARTA automated passenger count (APC) data were appended to the routes, and the energy use per passenger mile was compared across routes for the three periods. The results showed that the coupled effect of shift in transit frequency and decrease in ridership from 2019 to 2020 increased route-level energy use for more than 87% of the routes and per-passenger mile energy use for more than 98% of the routes. In 2021, although MARTA service had largely returned to pre-pandemic conditions, ridership remained in an early stage of recovery. Total energy use decreased to about the pre-pandemic level, but per-passenger energy use remained higher than pre-pandemic for more than 91% of the routes. The results confirm that while total energy use is more closely associated with trip schedules and routes, per-passenger energy use depends on both trip service and ridership. The results also indicated a need for data-based transit planning, to help avoid inefficiency associated with over-provision of service or inadequate social distancing protection caused by under-provision of service.View the NCST Project Webpage

Opening a new train station is considered a way to generate amenity in a neighborhood. However, as train lines extend from a central city to suburbs and remote places, train stations may generate disamenity depending on the local context. This study examines the externality of stations from a house valuation perspective. A mixed-effects model is employed to capture the varying relationship between house prices and distance to a nearby station. The results show that this relationship significantly varies by county, which leads to a house price premium in some counties and price discount in others. This study attributes the price discount (disamenity) to low ridership, seasonality of ridership, passenger traits, and long distance from a central city. The study results are expected to provide policymakers with balanced insights on establishing a new station so that train stations can serve as a local amenity, not a harmful facility in the neighborhood. [ABSTRACT FROM AUTHOR]

During the COVID-19 outbreak, serious concerns were raised over the risk of spreading the infection on public transportation systems. As the pandemic recedes it will be important to determine optimal timetable design to minimize the risk of new infections as systems resume full service. In this study, we developed an integrated optimization model for service line reopening plans and timetable design. Our model combines a space-time passenger network flow problem and compartmental epidemiological models for each vehicle and platform in the transit system. The algorithm can help policy makers to design schedules under COVID-19 more efficiently. The report develops an optimized timetable for the Bay Area Rapid Transit system. We found that if passengers choose other mode of transportation when closing part of the system or decreasing the frequency of service can prevent the spread of infections, otherwise, if passengers choose to use the closest open station, closings will lead to longer waiting times, higher passenger density and greater infection risk. We found that the goal of stopping the spread of infection could be achieved by minimizing the total delay when infections were similar in different districts across the service area. Where infection rates are different in different districts, minimizing the risk of exposure can be achieved by minimizing weighted travel time where higher weights are applied to areas where the infection rate is highest.

This research project examines the link between job access and stop/station level transit ridership. Job access, following recent literature, is measured as the number of jobs that can be reached within a 30-minute transit travel time, including transfers and walk time to access jobs once exiting a transit station. Cumulative opportunity job access measures of this sort—i.e., the number of jobs that can be reached within 30 minutes—have become common in the recent access literature, and those measures have often focused on access via transit. Yet there have been few studies that examine the link between transit job access and transit ridership, and of those none that examine the link at a station or stop level. This study uses station and stop level ridership data for the Los Angeles Metro bus and rail system and the BART rail system in the San Francisco Bay Area. The research team calculated transit job access as jobs that can be reached within 30 minutes, using the Remix software tool. Regression analysis of 1,000 randomly selected Los Angeles bus stops reveals a robust relationship between stop-level ridership and job access. The association between transit job access and bus stop ridership (embarkations and disembarkations at the stop) is statistically significant. Converting that association into an elasticity, if the number of jobs accessible within 30-minutes were to increase by 1 percent, on average stop-level ridership would increase between 0.6 to 0.8 percent. The same association, with similar magnitudes, exists for Metro rail stations and BART rail stations, but due the smaller sample sizes, those relationships are not statistically significant when control variables are added to the regression. The findings show that job access is closely related to ridership at the bus stop level, suggesting transit agencies can increase job access by increasing bus frequency, reducing transfers, siting lines that connect job concentrations to residents, and by improving bus stop/rail station access/egress times.View the NCST Project Webpage

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.

From March 2020 through March 2021, researchers monitored three San Francisco Bay Area transit agencies: two large – Alameda-Contra Costa Transit District (AC Transit), Valley Transportation Authority (VTA); and one small – Tri Delta Transit. As the lockdown was imposed in response to the COVID-19 pandemic, white-collar commuters, students, and the elderly stopped using public transit. Initially, ridership fell 90 percent, and then over the year slowly climbed to less than 50 percent for AC Transit and VTA, and to around 60 percent for Tri Delta Transit. The pace of recovery was not steady as ridership declined during protests in June 2020, during fare reinstatements in autumn 2020 and during the second COVID-19 wave in winter 2020-21. Agencies’ responses to the pandemic consisted of three parts: 1) maintaining health and safety of their employees; 2) minimizing COVID risk for their riders by keeping buses clean and enabling social distancing through capping the number of passengers on buses; and 3) changing their service. There was a direct relationship between the socioeconomic status of the population and transit ridership during the year studied. Higher ridership was observed in low-income areas with a high percentage of Latino, Black and Asian populations. These are generally renters, who do not have a car, but have to go to work either because they are essential workers and/ or are undocumented immigrants who cannot afford staying jobless. On the other hand, in wealthy areas of the Bay Area transit activity all but disappeared.

Transit agencies conduct system-level ridership forecasting for planning, budgeting, and other administrative purposes. However, the COVID-19 pandemic introduced substantial changes in transit ridership levels and seasonal patterns, which has impacted the performance of ridership forecasting. Although time series methods are commonly used for forecasting transportation demand, they have received limited use in practice for public transit ridership forecasting. This study compares the performance of seven time series forecasting methods for predicting system-wide, monthly transit ridership for heavy rail agencies in the continental United States. The forecasting methods are: ETS, ARIMA, STL with ETS, STL with ARIMA, TBATS, a neural network, and a hybrid model. Ridership was forecasted for pre- and post-COVID periods (pre- and post- March 2020), as well as for the full series (January 2002 to December 2023). The MAPE and MASE were used to compare forecast performance. Using the pre-COVID period, 43% of the models produced a MAPE below 5% and 82% produced a MAPE below 10%. Using the full-series and post-COVID periods, only about 10% of the models produced a MAPE below 5% and half produced a MAPE below 10%. The classical and hybrid methods outperformed the other models using the full series period, and the TBATS, neural network, and hybrid methods outperformed the other methods using the post-COVID period. The findings suggest that even a few years into the post-COVID era, patterns that were typical of heavy rail ridership before the pandemic have not returned at most agencies in the United States, posing challenges to forecasting post-COVID ridership.

AbstractRoad authorities are committed to improving community infrastructure through Bus Rapid Transit (BRT) projects. Yet, these initiatives often disrupt traffic, as seen in the Amman BRT project. A study assessed traffic conditions during various stages to address these disruptions, including simulated BRT operations. The research collected data on traffic volume, travel time, delays, and transit ridership before, during, and after construction and after BRT operations began. As a case study, the study focused on the impact of Amman’s first mass transit system at the Yajouz intersection. Traffic volumes decreased by 28% during construction, reducing delays significantly from 118.03 to 91.04 seconds. Although average speeds increased slightly (one km/h, on average), travel time doubled, negatively impacting 84% of businesses due to considering alternative routes for traveller destinations. However, delays significantly reduced after construction to 58 seconds per vehicle despite the higher traffic volume. Traffic simulations during construction underestimated observed delays (53.5 seconds per vehicle vs. 73.7 seconds per vehicle), still indicating a Level of Service (LOS) E. In BRT operation, modelled delays (55.8 seconds per vehicle) were lower than observed (63.8 seconds per vehicle), maintaining same LOS level. BRT minimally impacted ridership, an expected outcome given its trial phase. Environmental assessments revealed emissions peaking during construction, with a 25% modal shift to BRT yielding the lowest emissions. The study stresses sustainable urban mobility, advocating for meticulous planning, adaptable policies, and public awareness, emphasizing the need for detailed pedestrian research in this context.

In the current study, we examine the effects of the number of subway stations as well as magnitude of subway ridership on crime in 1/4-mile egohoods in New York City, while accounting for structural characteristics and land uses. Longitudinal analyses are conducted using a set of negative binomial regression fixed effects models. We found that the number of subway stations have crime-enhancing effects for robbery, aggravated assault, burglary, and larceny, but crime-reducing for motor vehicle theft. We found mixed findings for subway ridership across different crime types. We also found that the effects of subway stations are moderated by the proportion of retail land use in place. The findings provide evidence that subway stations and ridership have important consequences for the spatial patterns of crime. [ABSTRACT FROM AUTHOR]

The Los Angeles County Metropolitan Transportation Authority (Metro) has allocated significant resources (time, money, political) to rework their bus network through the NextGen initiative, the first sweeping overhaul of the bus system in 25 years. Because such overhaul of a bus system requires extensive resources, the next major restructuring of Metro’s bus routes will not occur for at least another decade or two. Thus, it is imperative that Metro aligns their bus service where people actually travel to, rather than merely supporting existing Metro routes. This project analyzes major travel patterns across L.A. County to spot any possible bus service recommendations explicitly not identified in NextGen. Major travel is assessed utilizing Metro’s location-based service (LBS) cell-phone database, Metro's ridership data, and Census “OnTheMap” data. Though our analysis corroborates NextGen’s study revealing most trips in LA County are under 5 miles, we found notable exceptions. For the exceptions, namely in the South Bay, Gateway Cities, and the San Fernando Valley, this project provides critical service opportunities to match Metro service to observed travel patterns. Finally, we suggest a pilot microtransit program, a low-cost investment, to test the actual demand strength of proposed service suggestions.

In this paper, a national-wide study is conducted to investigate the impacts of COVID-19 on the public transit ridership in the top twenty metropolitan areas in the U.S. At first, COVID-19 composite index was developed to qualitatively measure the level of public fear toward COVID-19 in different metropolitan areas. After that, to analyze the impact of COVID-19 and some socioeconomic factors on transit ridership reduction during the COVID-19 pandemic, a random-effects panel data model was developed and the traditional correlation analysis was also conducted. According to the results of both analyses, it was found that the areas with higher median household income, a higher percentage of the population with a Bachelor’s degree or higher, higher employment rate, and a higher percentage of the Asian population are more likely to have more reductions in public transit ridership during the COVID-19 pandemic. On the other side, the areas with a higher percentage of the population in poverty, and a higher percentage of the Hispanic population are more likely to experience smaller reductions in public transit ridership.

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.

This report presents and reviews the available sources of data on public transit riders and ridership. We intend it to be a resource for those who manage or simply wish to understand U.S. transit. In conducting this review, we consider the advantages and disadvantages of publicly available data on transit from a variety of public and private sources. We consider as well the relatively scarcer and less available sources of data on other providers of shared mobility, like ride-hail services, that compete with and complement public transit, as well as pieces we see as missing from the transit analytics pie. We conclude by discussing how data gaps both align with existing inequities and enable them to continue, unmeasured, and how the COVID-19 pandemic has made closing these gaps all the more important.

This report presents and reviews the available sources of data on public transit riders and ridership. We intend it to be a resource for those who manage or simply wish to understand U.S. transit. In conducting this review, we consider the advantages and disadvantages of publicly available data on transit from a variety of public and private sources. We consider as well the relatively scarcer and less available sources of data on other providers of shared mobility, like ride-hail services, that compete with and complement public transit, as well as pieces we see as missing from the transit analytics pie. We conclude by discussing how data gaps both align with existing inequities and enable them to continue, unmeasured, and how the COVID-19 pandemic has made closing these gaps all the more important.

Road authorities are committed to improving community infrastructure through Bus Rapid Transit (BRT) projects. Yet, these initiatives often disrupt traffic, as seen in the Amman BRT project. A study assessed traffic conditions during various stages to address these disruptions, including simulated BRT operations. The research collected data on traffic volume, travel time, delays, and transit ridership before, during, and after construction and after BRT operations began. As a case study, the study focused on the impact of Amman's first mass transit system at the Yajouz intersection. Traffic volumes decreased by 28% during construction, reducing delays significantly from 118.03 to 91.04 seconds. Although average speeds increased slightly (one km/h, on average), travel time doubled, negatively impacting 84% of businesses due to considering alternative routes for traveller destinations. However, delays significantly reduced after construction to 58 seconds per vehicle despite the higher traffic volume. Traffic simulations during construction underestimated observed delays (53.5 seconds per vehicle vs. 73.7 seconds per vehicle), still indicating a Level of Service (LOS) E. In BRT operation, modelled delays (55.8 seconds per vehicle) were lower than observed (63.8 seconds per vehicle), maintaining same LOS level. BRT minimally impacted ridership, an expected outcome given its trial phase. Environmental assessments revealed emissions peaking during construction, with a 25% modal shift to BRT yielding the lowest emissions. The study stresses sustainable urban mobility, advocating for meticulous planning, adaptable policies, and public awareness, emphasizing the need for detailed pedestrian research in this context. [ABSTRACT FROM AUTHOR]

Increasing transit use has many benefits, including reducing traffic congestion and greenhouse gas (GHG) emissions. However, riders need to be able to get to a station in order to use transit. Walking is an option only for those within a limited radius of a station. Driving to a station may be feasible for some, but providing sufficient parking can be expensive and land intensive. The rise of ride-hailing companies such as Uber and Lyft presents a new opportunity for bridging the “first-mile” gap to high quality transit. Transit agencies are beginning to launch pilot projects to test public-private partnerships with ride-hailing companies to increase access to transit.This policy brief summarizes findings from researchers at UC Davis who used existing modeling tools and data to understand the potential market demand for a first-mile transit access service in the San Francisco Bay Area. They modeled the likelihood of commuters who drive alone to switch to using ride-hailing and the Bay Area Rapid Transit (BART) rail system to get to work based on travel time, cost, and distance to a BART station. They explored the magnitude of change in overall travel time and cost for travelers who switch from driving alone to using ride-hailing and BART, as well as potential changes to vehicle miles traveled (VMT) and GHG emissions at both the regional and station level.View the NCST Project Webpage

This research brief summarizes the findings from the associated study, which examined the potential to improve transportation system efficiency by reevaluating and redefining the role of public transit and its design principles in the new context of technology and shared mobility. Specifically, it evaluated the financial feasibility of an integrative, multimodal transportation system where public and private mobility services coexist to maximize economic and environmental benefits and free up public transit resources to be reallocated more efficiently.

Background: People invest hours of their working day to pay for their vehicle. Is this a worthwhile use of their time? Wouldn't public transport be a more economical solution for those on low income? Which demographic group typically chooses the most effective mode of transport? Aim: The two studies presented in this paper answer those fundamental questions based on the effective speed concept. This is calculated by dividing the distance travelled by time (i.e., minutes spent travelling and earning the money to afford it). Methodology: A digital model of the residents in Berlin is created to calculate the 'effective speed' for everyone. In the second study, based on a survey of residents in New York City, the best mode of transport for each respondent based on their effective speed has been identified and compared to the mode chosen. Results: The results of the Berlin case study show that the mode share for cars would be less than 6% if people choose the mode of transport with the highest effective speed. A similar picture is seen in NYC. Factors that significantly affect whether people who should use public transport, do so, include: age, gender, education, place of work or home. [ABSTRACT FROM AUTHOR]

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. • Study pandemic abandonment of transit, intentions to return to riding transit, and likelihood of increased ridership with fare integration. • Assess the role of sociodemographics, employment characteristics, transit investment priorities, and travel behavior. • Compare the ridership return intentions by multiple transit agency types (e.g. urban/suburban, bus/rail). • Teleworking, vehicle access, racial or ethnic minority status or priority for sanitation of transit facilities impact future ridership intentions. • Discussion of how transit can attract vulnerable customer segments during the pandemic recovery phase. [ABSTRACT FROM AUTHOR]

Amidst the rapid urbanization in the country, the importance of urban public transport has been realized by the governments. Public transport is vital to the city's economy and ensures social equity. Bus Rapid Transit System (BRTS) has emerged as an exciting way to decongest the cities, enhancing and increasing mobility and accessibility and promoting transit-oriented development. India's longest BRTS, Janmarg, was created with similar objectives. However, the system faces reduced ridership and inhibited growth because of its hidden inefficiencies. This research aims to identify all the factors affecting ridership, accessibility, and inhibiting the system's growth even after having sufficient infrastructure. It also aims to quantitatively determine whether Janmarg could achieve its establishment's objectives on the city transportation system after thirteen years of the successful implementation of the project. The research methodology used in this work combines primary and secondary research. The factors are responsible for preventing Janmarg from achieving its objectives and utilizing its full potential are determined and recommended ways to utilize adversities effectively. Results show that this study endorses reducing ambiguities and a way forward for making a sustainable transportation system.

This study investigates barriers to accessing the Davis Amtrak Station and will inform a pilot program to increase access to the station with on-demand alternatives. The program aims to decrease private vehicle use to access the station and for travel to locations outside of Davis. Currently, the Davis station has the third highest passenger usage along the Capitol Corridor and ridership is increasing, but potential ridership is limited by parking availability. The city has limited interest in or ability to add new parking capacity, however pricing will be introduced to the lot in the future. In addition, the city will introduce a pilot program to support the use of on-demand alternatives and other modes to access the Davis station. The goal of this phase of the study was to develop surveys for Davis residents and current Capital Corridor riders that will assess barriers to use of the station. Such factors likely include limited vehicle and bicycle parking, limited local bus service hours, last mile challenges at the other end of the trip, and the convenience of driving for regional travel. The results of this survey will offer insights into the key factors influencing the use of the Capitol Corridor regional rail, and the importance of station access among those factors. Further, this phase of the study will inform the development and implementation of the city’s pilot program.

Ideally, public transit, by moving more people using fewer vehicles, serves as a backbone of a transportation system. However, most transit systems in the United States suffer from low ridership and high operating costs, thus they provide a significantly compromised mobility service to the transportation system users. Under current transit system design principles, such as service area requirements, inefficiencies in resource use are almost inevitable. Given the opportunities brought by new mobile technology and the environment of mobility as a service, current transit system design principles need to be reevaluated and redefined to enable transit to serve as a backbone in the transportation system. In this seed-grant project, the researchers evaluated whether building an integrated multimodal public transportation system via reallocation of transit resources is financially feasible and environmentally sustainable. They also conducted an in-depth review of related literature and discussed other concerns regarding an integrated system. Based on the results from the case study and review of other recent studies, the authors draw an optimistic conclusion about an integrated service system where public and private mobility service providers coexist.

Over the past decade, while public transit operators in the Los Angeles region started experiencing steep declines in ridership, Pasadena Transit was adding riders. More recently, however, Pasadena Transit’s ridership plateaued and then started to decrease. The Transit Division of the City of Pasadena Department of Transportation is therefore interested in understanding what may be causing transit ridership to decline in its jurisdiction, what are the circumstances driving these changes, and what these causes and circumstances suggest for ways to make the system more attractive to riders.

Gradual increase in the pace of urbanization has brought an increase in demand for rapid transit systems throughout the nation. An increase in traffic congestion on roads has led to the recent advent of the Mass rapid transit system thereby accelerating the commuter's interest to prefer them over the conventional railway system. Over the past few years, rapid transit systems like metros have been trying to maintain a steady count of ridership by attracting new commuters while retaining the existing commuters. Thus, it has become routine for organizations to identify the service parameters that serve as the essence of commuter satisfaction or dissatisfaction by implementing additional necessary changes based on commuter perceptions. The study is focused on the service quality measures adopted by the Chennai metro rail operations which have led them to maintain a steady pace of passengers amidst the pandemic season. The resultant statistical analysis and fuzzy evaluation method are confined to about 74.6% indicating decent fitness of the sample size. The significance value of coach conditioning amenities is 0.002 and the ambiance facility is 0.003, further indicating the need to maintain their service pace. Hospitality facility that accounts for a significance value of about 0.000 needs the most improvement to influence the overall service quality satisfaction of commuters towards improving their ridership statistics.

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. [ABSTRACT FROM AUTHOR]

Public rail transport infrastructure has been developed to address urban issues in some major cities worldwide, including Jakarta, Indonesia. Despite its development, the rail transit ridership is not optimal yet, since traffic flows on the roads are still heavy and cause congestion. Therefore, the modal shift on rail transit in should be increased. The objective of this study is twofold; determining factors affecting the mode split on rail transit and exploring strategies to improve the modal shifting to rail-based transportation by taking into account the development of properties near the transit stations of Greater Jakarta's commuter line, including hotel, retail, office, and residential. Both quantitative and qualitative methods through questionnaire survey and benchmarking study were used to obtain these objectives. The survey results show that punctuality is the factor that affects the modal shifting to rail the most, followed by train comfort and shorter waiting time. Benchmarking studies were conducted to develop strategies for improving the train performance in terms of those factors, which include utilizing planning and management tool for train scheduling and rail infrastructure control, improving the air conditioning system on board trains, and increasing the information availability by installing real-time information monitors. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has caused an unprecedented decrease in transit ridership around the world. Some major cities in the United States have lost over 80% of their transit riders, and some transit agencies have been forced to reduce service and work with a skeleton crew at times. Although the future of the COVID-19 pandemic and its long-term effect on transit systems are still unclear, much can be learned from previous disruptive events, such as terrorist attacks and epidemics, in the past 30 years, so as to draw qualitative and quantitative insights about the public reactions, ridership recovery periods, and transit agency responses during and after those events. The goal is to facilitate the understanding of the current COVID-19 pandemic and inform future decisions of transit agencies. This paper provides a historic review of the impacts of terrorist attacks such as the London and Madrid bombing, the Tokyo sarin gas, and the 9/11 attacks; and past epidemics and pandemics, such as Severe Acute Respiratory Syndrome (SARS), H1N1 swine flu, Middle East Respiratory Syndrome (MERS), and Ebola. It was found that most of these reviewed incidents (except for the 9/11 attacks) did not impose prolonged post-event effects on transit ridership for more than one year. In addition, executive orders (e.g., school closures), transportation service levels (e.g., screening time for air travel and rail station closures), public fear, media reports, and reduced tourists were frequently mentioned as key factors that impacted transit ridership. The review also identified measures that could potentially help restore transit ridership, such as sanitizing vehicles and facilities, adjusting services, and launching promotions and advertisements. Finally, insights on the ongoing COVID-19 pandemic are drawn through comparisons with historical events.

Although the COVID-19 pandemic highly impacted transit ridership as people reduced or stopped travel, these changes occurred at different rates in different regions across the United States. This study explores the impacts of COVID-19 on ridership and recovery trends for all federally funded transit agencies in the United States from January 2020 to June 2022. The findings of this analysis show that overall transit ridership hit a 100-year low in 2020. Changepoint analysis revealed that June 2021 marked the beginning of the recovery for transit ridership in the United States. However, even by June 2022, rail and bus ridership were only about two-thirds of the pre-pandemic levels in most metropolitan statistical areas (MSAs). Only in a handful of MSAs like Tampa and Tucson did rail ridership reach or exceed 2019 ridership. This retrospective study concludes with a discussion of some longer-term changes likely to continue to impact ridership, such as increased telecommuting and operator shortages, as well as some opportunities, such as free fares and increased availability of bus lanes. The findings of this study can help inform agencies about their performance compared to their peers and highlight general challenges facing the transit industry.