Subway Ridership Research Articles

Spatial and Temporal Variation of Subway Ridership before and during the COVID-19 Period in Beijing

The outbreak of COVID-19 in 2019 caused a huge impact on people’s lives. Uncovering the variation of public traffic daily patterns during the pre-pandemic and pandemic periods would help interpret the impact of the pandemic on people’s routine activity and promote the sustainable development of public transport systems. By collecting subway traffic data during the pre-pandemic and pandemic periods in Beijing, China, this paper analyzes the spatial and temporal variation of subway ridership and seeks to find out what sort of environment variables related to the variation of subway ridership during the two periods. The results show that the ridership of Beijing subway during the pandemic period decreased by 91.69% compared with the pre-pandemic period. On working days and non-working days during the pandemic period, the subway stations experiencing huge ridership reductions were mainly distributed within the core urban areas, while in the morning peak hours, the stations experiencing huge ridership reduction were located within suburban areas. The origin-destination stations with a large decrease in ridership were mainly distributed along the central to northern directions of Beijing but, on non-working days, they were mainly distributed along the central to northwestern directions of Beijing. The results of the regression analysis indicated that, during the pandemic period, the industrial parks were significantly positively correlated with subway ridership, while the pedestrian road network density was significantly negatively correlated with subway ridership.

Impact Evaluation of COVID-19 on Transit Ridership: A Case Study of the Beijing Subway

A comprehensive understanding of the multifaceted ramifications of the coronavirus disease 2019 (COVID-19) on transit ridership is imperative for the optimization of judicious traffic management policies. The intricate influences of this pandemic exhibit a high degree of complexity, dynamically evolving across spatial and temporal dimensions. At present, a nuanced understanding remains elusive regarding whether disparate influencing factors govern inbound and outbound passenger flows. This study propels the discourse forward by introducing a methodological synthesis that integrates time series anomaly detection, impact inference, and spatiotemporal analysis. This amalgamation establishes an analytical framework instrumental in elucidating the spatiotemporal heterogeneity intrinsic to individual impact events, grounded in extensive time series data. The resulting framework facilitates a nuanced delineation, affording a more precise extraction of the COVID-19 impact on subway ridership. Empirical findings derived from the daily trip data of the Beijing subway in 2020 substantiate the existence of conspicuous spatiotemporal variability in the determinants influencing relative shifts in inbound and outbound ridership. Notably, stations situated in high-risk areas manifest a conspicuous absence of correlation with outbound trips, exhibiting a discernibly negative impact solely on inbound trips. Conversely, stations servicing residential and enterprise locales demonstrate resilience, evincing an absence of significant perturbation induced by the outbreak.

Cost-effectiveness of mask mandates on subways to prevent SARS-CoV-2 transmission in the United States.

Community-based mask wearing has been shown to reduce the transmission of SARS-CoV-2. However, few studies have conducted an economic evaluation of mask mandates, specifically in public transportation settings. This study evaluated the cost-effectiveness of implementing mask mandates for subway passengers in the United States by evaluating its potential to reduce COVID-19 transmission during subway travel. We assessed the health impacts and costs of subway mask mandates compared to mask recommendations based on the number of infections that would occur during subway travel in the U.S. Using a combined box and Wells-Riley infection model, we estimated monthly infections, hospitalizations, and deaths averted under a mask mandate scenario as compared to a mask recommendation scenario. The analysis included costs of implementing mask mandates and COVID-19 treatment from a limited societal perspective. The cost-effectiveness (net cost per averted death) of mandates was estimated for three different periods based on dominant SARS-CoV-2 variants: Alpha, Beta, and Gamma (November 2020 to February 2021); Delta (July to October 2021); and early Omicron (January to March 2022). Compared with mask recommendations only, mask mandates were cost-effective across all periods, with costs per averted death less than a threshold of $11.4 million (ranging from cost-saving to $3 million per averted death). Additionally, mask mandates were more cost-effective during the early Omicron period than the other two periods and were cost saving in January 2022. Our findings showed that mandates remained cost-effective when accounting for uncertainties in input parameters (e.g., even if mandates only resulted in small increases in mask usage by subway ridership). The findings highlight the economic value of mask mandates on subways, particularly during high virus transmissibility periods, during the COVID-19 pandemic. This study may inform stakeholders on mask mandate decisions during future outbreaks of novel viral respiratory diseases.

Trends in police complaints and arrests on New York City subways, 2018 to 2023: an interrupted time-series analysis

BackgroundPublic transportation use is influenced by perceptions of safety. Concerns related to crime on New York City (NYC) transit have risen following NYC’s COVID-19 pandemic state of emergency declaration in 2020, leading to declines in subway ridership. In response, the most recent mayoral administration implemented a Subway Safety Plan in 2022. This study aimed to quantify the effects of the COVID-19 pandemic and the Subway Safety Plan on rates of complaints to and arrests by the New York City Police Department (NYPD) Transit Bureau.MethodsUsing publicly available data on complaints and arrests, we conducted interrupted time-series analyses using autoregressive integrated moving average models applied to monthly data for the period from September 2018 to August 2023. We estimated changes in the rates of complaints to and arrests by the NYPD Transit Bureau before and after: (1) the COVID-19 pandemic state of emergency declaration (i.e., March 2020), and (2) the announcement of the Subway Safety Plan (i.e., February 2022). We also examined trends by complaint and arrest type as well as changes in proportion of arrests by demographic and geographic groups.ResultsAfter the COVID-19 pandemic declaration, there was an 84% increase (i.e., an absolute increase of 6.07 per 1,000,000 riders, CI 1.42, 10.71) in complaints to the NYPD Transit Bureau, including a 99% increase (0.91 per 1,000,000 riders, CI 0.42, 1.41) in complaints for assault and a 125% increase in complaints for harassment (0.94 per 1,000,000 riders, CI 0.29, 1.60). Following the Subway Safety Plan there was an increase in the rate of arrests for harassment (0.004 per 1,000,000 riders, CI 0.001, 0.007), as well as decreases in the proportion of arrests for individuals racialized as White (− 0.02, CI − 0.04, − 0.01) and proportion of arrests in the borough of Manhattan (− 0.13, CI − 0.17, − 0.09).ConclusionsThe increased rates of complaints to the NYPD Transit Bureau following the onset of the COVID-19 pandemic remained elevated following the enactment of the Subway Safety Plan. Further evaluation efforts can help identify effective means of promoting safety on public transportation.

Still prefer, but not that much: the premium of subway access on house prices during the COVID-19 pandemic period

ABSTRACT Rail access has traditionally been considered a factor that enhances the amenity value of urban areas, resulting in a price premium for properties located near these stations. However, the Covid-19 pandemic has exerted a profound influence on people's mobility patterns and travel behavior within urban areas. One of the most conspicuous changes is a substantial decrease in subway ridership. This study investigates whether the decline in public transit ridership has led to shifts in housing location preferences driven by proximity to rail access during the Covid-19 pandemic. This study focuses on assessing the impact of the pandemic on the premium associated with rail access in relation to house prices. Using approximately 180,000 apartment transaction records from Seoul, South Korea, the empirical findings, based on a difference-in-differences approach, reveal an immediate and significant depreciation effect resulting from this exogenous shock during the 25-month pandemic period. The premium decreases significantly within a 1.2-kilometer radius from a subway station, and this reduction exhibits a distance-decay pattern within the designated area. Highlights Traditional amenity value of rail access challenged by Covid-19. Profound decline in subway ridership during pandemic. Investigation into pandemic's impact on housing location preferences. Assessment of pandemic's effect on rail access premium. Immediate and significant depreciation effect within 1.2 km from subway stations.

Navigating the post-pandemic urban landscape: Disparities in transportation recovery & regional insights from New York City

The onset of the global Covid-19 pandemic in early 2020 brought many transportation systems in North America to a standstill. As life returned to normal, various modes of transportation exhibited differing rates of recovery, with disparities across regions. Limited research has delved into the regional variations in the recovery of these modes of transit over the past years. Such analysis is crucial for gaining insights into urban recovery and resilience, as well as understanding the factors influencing such recovery. In this work, we investigate the usage recovery of taxis, ride-hailing services, and subway ridership following the Covid-19 pandemic. We focus on New York City as our case study, employing clustering techniques to identify neighborhoods with similar recovery patterns. Furthermore, we examine the socio-economic, demographic, and built-environment factors contributing to regional variations in this recovery. Our research findings reveal that different modes of transportation responded differently to the pandemic, and these responses exhibited regional disparities. These findings hold significance for future health-related emergency response strategies and the regulation of existing transportation infrastructure.

The impacts of COVID-19 on route choice with guidance information in urban rail transit of megacities

The outbreak of COVID-19 has caused unprecedented decline of ridership in urban rail transit and changed passenger travel habits, which greatly challenges subway operations. Therefore, it is necessary to better understand and quantify the impact of COVID-19 on passenger travel behavior, specifically route choice. Thus, we collected automatic fare collection data and 2060 random samples through a web-based survey in Beijing on passengers’ route choice behavior during the COVID-19 pandemic. This study utilizes an initial dataset to conduct an analysis and introduces an improved Generalized Random Regret Minimization model (GRRM) aimed at understanding passengers' route choice adjustments in response to COVID-19 guidance information. This improved GRRM accounts for two decision-making criteria, namely, maximum utility and minimum regret, and considers passenger heterogeneity. This marks the first instance of capturing the heterogeneity shift effect in route choice perception during the COVID-19 pandemic. The results show that the improved model has the best fitting result with adjusted Rho square of 0.536, demonstrating that the attributes related to guidance information (i.e., information push/time to receive traffic information/perceived route COVID-19 risk) indeed enhance the model’s fit. Furthermore, the research employs Value of Information Time to quantify the preference of passengers for information in various groups. Compared with the usual scenario, women, young and non-commuter passengers are more likely to receive an early information update to plan their trips in advance. Finally, the perceived risk of COVID-19 on routes is examined in relation to passengers’ personal attributes. It is observed that the elderly and students exhibit heightened sensitivity to the epidemic at all stages, while young passengers and commuters are particularly sensitive only during the small-scale epidemic. These findings offer valuable insights for managers to implement targeted strategies, thereby enhancing passenger flow control and encouraging increased subway ridership.

Exploring the influence of relative attractiveness in green spaces on urban movements: A potential to kinetic energy framework

Urban green spaces are increasingly recognized for their positive impact on well-being, quality of life, and public health. Researchers have explored how green space characteristics, such as size, theme, and convenience, influence urban movement. However, an important aspect that has received limited attention is the relative attractiveness of green spaces. When residents have access to similar green spaces nearby, their inclination to visit more distant ones decreases. This leads to the central research question: How does the relative attractiveness of urban green spaces affect urban movements? The existing literature lacks a comprehensive theory to address this question. To bridge this gap, this paper introduces the Potential to Kinetic Energy (PKE) framework, inspired by principles from physics. In this framework, potential energy represents the relative attractiveness of green spaces between the travel origin and destination, while kinetic energy signifies the urban movement resulting from the this attractiveness. To demonstrate the PKE framework's applicability, this paper analyzes green space data and smart card subway ridership records from Beijing. The results show that a unit increase in size, cultural, attraction, proximity, and location potential energy is associated with a 0.22%, 0.27%, 0.14%, 0.19%, and 0.02% increase in urban movement. The findings provide compelling evidence for the significant role of relative attractiveness in shaping urban movements and offer valuable insights for urban planning and policy development. The proposal of the PKE framework encourages interdisciplinary discussions among urban planners, environmental scientists, and transportation experts, fostering collaborative efforts to enhance the sustainability of cities.

Urban Mobility in the Postpandemic Stage: A Comprehensive Investigation of a Variety of Cities in China

The global outbreak of coronavirus disease 2019 (COVID-19) has affected the urban mobility of nations around the world. The pandemic may even have a potentially lasting impact on travel behaviors during the post-pandemic stage. China has basically stopped the spread of COVID-19 and reopened the economy, providing an unprecedented environment for investigating post-pandemic travel behaviors. This study conducts multiple investigations to show the changes in travel behaviors in the post-pandemic stage, on the basis of empirical travel data in a variety of cities in China. Specifically, this study demonstrates the changes in road network travel speed in 57 case cities and the changes in subway ridership in 26 case cities. Comprehensive comparisons can indicate the potential modal share in the post-pandemic stage. Further, this study conducts a case analysis of Beijing, where the city has experienced two waves of COVID-19. The variations in travel speed in the road network of Beijing at different stages of the pandemic help reveal the public’s responses towards the varying severity of the pandemic. Finally, a case study of the Yuhang district in Hangzhou is conducted to demonstrate the changes in traffic volume and vehicle travel distance amid the post-pandemic stage based on license plate recognition data. Results indicate a decline in subway trips in the post-pandemic stage among case cities. The vehicular traffic in cities with subways has recovered in peak hours on weekdays and has been even more congested than the pre-pandemic levels; whereas the vehicular traffic in cities without subways has not rebounded to pre-pandemic levels. This situation implies a potential modal shift from public transportation to private vehicular travel modes. Results also indicate that commuting traffic is sensitive to the severity of the pandemic. This may be because countermeasures, e.g., work-from-home and suspension of non-essential businesses, will be implemented if the pandemic restarts. The travel speed in non-peak hours and on non-workdays is higher than pre-pandemic levels, indicating that non-essential travel demand may be reduced and the public’s vigilance towards the pandemic may continue to the post-pandemic stage. These findings can help improve policymaking strategies in the post-pandemic new normal.

Analysis and Prediction of Subway Ridership

Many people go by subway in China. Huge passenger flow brings much trouble to the police and passengers, such as crowded carriages, long waiting times and low-efficiency transport. So it is important to know the traffic jam before it brings many problems. With the data from Tianchi Competition, this paper analyzes and predicts the subway ridership of a station in Hangzhou based on time series and linear regression. Taking line 3, station 5 as an example, there were two peaks which have many passengers in the station. Combined with the results, it proposed that traffic police need to pay more attention to the rush hours. People who do not need to commute can avoid these times. The government may also use this prediction results in the subway service management, as well as in the planning for the future development and subway lines projection.

Impact of Covid-19 Prevalence in Seoul on Subway Usage

This article shows that there is a negative impact of government’s social distancing policy, daily COVID-19 confirmed cases, daily volume of new articles related to COVID-19, and a weekend dummy on subway ridership in Seoul, Korea. Multiple regression with OLS is applied to 289 daily data mostly collected from the Open Government Data Portal during the early stages of COVID-19 pandemic, from January 1 to October 30, 2020. The model is run separately for four subway station categories: commercial, office, residential, and other area. Different impacts of explanatory variables across different area have some policy and managerial implications. This study concludes with future research.

Exploring the Role of Transit Ridership as a Proxy for Regional Centrality in Moderating the Relationship between the 3Ds and Street-Level Pedestrian Volume: Evidence from Seoul, Korea

The preference for walking and the resulting pedestrian activities have been considered key success factors for streets, neighborhoods, and cities alike. Although micro- and meso-scale built environment factors that encourage walking have been investigated, the role of macroscopic factors such as regional centrality in explaining street-level pedestrian volume is often neglected. Against this backdrop, this study examines the relationship between built environments and street-level pedestrian volume using Smart Card and pedestrian volume survey data from Seoul after controlling for transport ridership as a proxy for regional centrality. As a preliminary study, we analyzed 36 regression models applying different sets of transit ridership variables and found that the combination of bus ridership within 400 m and subway ridership within 300 m best explained the variation in pedestrian volume on a street. Then, the effects of the 3D variables (density, diversity, and design) on pedestrian volume were compared before and after controlling for ridership within this spatial range. The results demonstrated that, after taking transit ridership into account, the influence of built environment variables is generally reduced, and the decrease is more pronounced among walkshed-level 3D variables than street-level variables. Particularly, while the effect of “design” (street connectivity) on pedestrian volume appeared to be negatively significant in the constrained model, it was found to be insignificant in the unconstrained model which controlled for transit ridership. This suggests that the degree of street connectivity is influenced by regional centrality, and accordingly, the coefficient of the “design” variable in our constrained model might be biased. Thus, to accurately understand the effect of the meso-scale 3D variables on pedestrian volume, both micro- and macro-scale built environmental factors should be controlled.

Relationship between built environment characteristics of TOD and subway ridership: A causal inference and regression analysis of the Beijing subway

Numerous studies suggest that built environments have impacts on transit ridership, but few consider the causal connection between them. The goal of this study is to examine the causal relationship between the built environment and subway ridership and then to re-examine the impacts. In the case of the Beijing subway system, we use the Bayesian Network learning approach to examine the causal relationship between built environment characteristics and ridership. Based on the causality analysis, we further explore the impact of the built environment on subway ridership using Ordinary Least Squares and Geographically Weighted Regression models. Findings reveal the causal impact of employment density and public transport accessibility on alighting ridership during the morning peak. The result of the correlation analysis between the morning-peak alighting ridership and other variables shows that higher employment density and public transport accessibility produce more travel demand. The regression model also indicates that the effects of the built environment on ridership vary across space. Last but not least, the results of the model performance tests show that the model constructed from the indicators obtained from the causality screening is reliable.

Subway Station and Neighborhood Crime: An Egohood Analysis Using Subway Ridership and Crime Data in New York City

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.

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.

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.

Viability of compact cities in the post-COVID-19 era: subway ridership variations in Seoul Korea.

COVID-19 exposed the vulnerability of compact cities against shock events. As the impact of COVID-19 not only persists, but also expands throughout the world, this study questions whether the compact city model would be sustainable in the post-COVID-19 era. As such, this study examines the dynamics among major COVID-19 outbreak events, government interventions, and subway ridership in two compact cities, Seoul and New York City. Then, to gain thorough understanding of the impact of risks on compact urban form, it narrows the scope to Seoul in comparing subway ridership patterns in 2019 and 2020, and identifying characteristics that affect the volatility of subway ridership levels. The results affirm that individual mobility, COVID-19 outbreaks, and government interventions are closely related, and reveal that the extent of social distancing measures in compact cities is limited. This finding aligns with existing literature that link diseases transmission with dense population and mixed land use, accentuating the vulnerability of the compact city model against shocks. As a result, a multidimensional urban planning approach that incorporates polycentric and decentralized urban form is recommended to effectively and sustainably control disease outbreaks in compact cities.

Impacts of COVID-19 Pandemic on Travel Behavior in Large Cities of China: Investigation on the Lockdown and Reopening Phases

The COVID-19 pandemic has caused worldwide lockdowns and similar containment measures aiming to curb the spread of the virus. Lockdown measures have been implemented in cities amid the COVID-19 outbreak. After the pandemic is under control, cities will be gradually reopened. This study aims to investigate the variations in urban travel behavior during the lockdown and reopening phases. On the basis of long-term traffic congestion index data and subway ridership data in eight typical cities of China, this study carried out comparisons on urban travel behaviors with and without the pandemic. Changes in the multimodal travel behaviors in different times of day and days of week are analyzed during the lockdown and reopening phases. Multivariate and one-way analyses of variance are conducted to show the statistical significance of the changes. This study further investigates the relationship between the returned-to-work (RTW) rate and travel behaviors in the reopening phase. A stepwise multiple regression is conducted to quantify the impacts of influencing factors (i.e., population migration index, RTW rate, socioeconomic indices, and pandemic statistical indicators) on vehicular traffic after reopening. Results show that the lockdown measure has a significant impact on reducing the traffic congestion during the peak hours on workdays, and the subway ridership dropped to below 10% of the prepandemic level during the lockdown phase. Travel demands tended to switch from subways to private vehicular travel modes during the reopening phase, leading to a rapid recovery of vehicular traffic and a slow recovery of subway ridership. The recovery of vehicular traffic is proved to be related to the RTW rate, certain city characteristics, and new COVID-19 cases after city reopening.

Critical Role of the Subways in the Initial Spread of SARS-CoV-2 in New York City.

We studied the possible role of the subways in the spread of SARS-CoV-2 in New York City during late February and March 2020. Data on cases and hospitalizations, along with phylogenetic analyses of viral isolates, demonstrate rapid community transmission throughout all five boroughs within days. The near collapse of subway ridership during the second week of March was followed within 1–2 weeks by the flattening of COVID-19 incidence curve. We observed persistently high entry into stations located along the subway line serving a principal hotspot of infection in Queens. We used smartphone tracking data to estimate the volume of subway visits originating from each zip code tabulation area (ZCTA). Across ZCTAs, the estimated volume of subway visits on March 16 was strongly predictive of subsequent COVID-19 incidence during April 1–8. In a spatial analysis, we distinguished between the conventional notion of geographic contiguity and a novel notion of contiguity along subway lines. We found that the March 16 subway-visit volume in subway-contiguous ZCTAs had an increasing effect on COVID-19 incidence during April 1–8 as we enlarged the radius of influence up to 5 connected subway stops. By contrast, the March 31 cumulative incidence of COVID-19 in geographically-contiguous ZCTAs had an increasing effect on subsequent COVID-19 incidence as we expanded the radius up to three connected ZCTAs. The combined evidence points to the initial citywide dissemination of SARS-CoV-2 via a subway-based network, followed by percolation of new infections within local hotspots.

Improved Approach for Forecasting Extra-Peak Hourly Subway Ridership at Station-Level Based on LASSO

AbstractPrediction of the extra-peak hourly ridership (EPHR) is directly related to the capacity design of subway station service facilities. In the traditional station-level EPHR prediction proces...