Metro Travel Research Articles

“Right here waiting”? Distribution of intentional metro travel groups amid COVID-19 in Hong Kong

During COVID-19, people needed to travel and meet as a group physically to gain benefits such as overcoming loneliness. Using multiday continuous smartcard data, we can identify intentional travel groups (ITGs) in cities. Using the 2020 smartcard data of the Hong Kong metro system, we first construct a dataset of probable ITG trips (PIGTs) where two riders swiped in and out at the same stations within one minute during weekends—we choose weekends because there are fewer co-presenting metro commuters entering and/or exiting the same station. Assuming that ITGs would have more PIGTs and smaller exit time difference than those co-presenting by coincidence, we then identified ITGs as those with at least four PIGTs in a month. To see whether and how the pandemic impacted ITGs, we geo-visualize the quantity of the identified ITGs by metro station and between metro stations on three different Sundays before and during the pandemic. The results indicate that ITGs’ distribution by metro station and between metro stations changed notably after the outbreak of COVID-19.

Multi-group exploration of the built environment and metro ridership: Comparison of commuters, seniors and students

Understanding the associations between demographic groups’ metro travel behaviors and the built environment is crucial for addressing automobile dependence and promoting transportation equity and reasonable urban construction. This study examines the nonlinear relationships and threshold effects of the built environment on the metro travel patterns of three groups (i.e., commuters, seniors, and students) by applying smart card data in Kunming, China. We select the optimal machine learning model—gradient boosting decision trees (GBDTs)—and consider various built environment attributes. Our findings indicate that: 1) built environment attributes universally have nonlinear and threshold effects on metro travel for all groups; 2) the collective contributions of density and diversity differ greatly across groups compared to other attributes; and 3) only a few built environment attributes have similar effect directions and degrees across all three groups, while most have unique effects on each group. The findings suggest metro station area planning strategies to promote metro use and transportation equity for different groups.

Nonlinear impact of built environment on people with disabilities’ metro use behavior

Ensuring equal access to public transportation for people with disabilities is crucial for social equity and sustainable urban development. The physical characteristics and travel preferences of people with disabilities may lead to unique spatial and temporal characteristics of metro use behavior and make them more likely to be influenced by built environment characteristics. We tested this hypothesis by comparing metro travel behavior in Wuhan, China, for people with and without disabilities. The results showed that people with disabilities take more and shorter metro trips than people without disabilities. The relative importance of built environment features in influencing metro ridership displays notable distinctions between people with disabilities and those without, as indicated by the outcomes of the gradient boosting decision tree (GBDT) model. For metro ridership among people with disabilities, street density, the number of medical facilities, and the building plot ratio are the top three influencing variables on weekdays, while the number of shopping centers, medical facilities, and recreational facilities are the top three on weekends. Additionally, these variables exhibit clear nonlinear associations on metro ridership for both groups. The findings offer new insights for providing a better travel environment for people with disabilities and thus promoting transportation equity.

Non-linear impact of the built environment on metro commuter flows before and after the COVID-19 outbreak: A case study in Guangzhou

The built environment is a crucial factor influencing subway commuting. However, the extent to which the built environment affects metro travel during the COVID-19 pandemic remains under explored. In particular, people's travel patterns and destinations might have changed during the pandemic, influenced by the built environment. The degree to which these influences differ from those in normal circumstances has scarcely been studied. This study investigates the non-linear effects of the built environment on metro commuters within an 800-m radius of subway stations. Analysis of mobile signal data from Guangzhou Metro Line 1 showed that the importance of the built environment was 81.69% and 70.06% before and after the outbreak, respectively. Based on the threshold effects of the built environment on subway passenger flows before and after the pandemic, this study determined the immediate needs of commuters for the built environment after the outbreak. This information can guide planning strategies around subway stations facing public health emergencies. This study highlights the non-linear impact of the built environment on subway passenger flows after an outbreak. Thus, the number of parking lots, the proportion of medical facilities and the distance to the central business district are urgent demands for subway passengers after the outbreak.

Urban spatial cluster structure in metro travel networks: An explorative study of Wuhan using big and open data

Urban spatial cluster structure in metro travel networks: An explorative study of Wuhan using big and open data

Joint optimization of metro travel reservation and compartment capacity allocation for an overcrowded metro line

ABSTRACT In some mega cities, metro lines often experience severe congestion during peak hours, posing serious operational risks. To alleviate passenger delay and address overcrowded conditions, this study proposes a novel model for metro travel reservation and carriage reserved capacity. The model comprises three components: off-station queuing restrictions, dynamic loading constraints, and carriage reserved capacity constraints. A particle swarm optimization algorithm is applied to allocate reservation quota by considering time-variant and location-dependent passenger demand and train supply. The paper applies the model to Nanjing Metro Line 3, using historical data to estimate passenger demand and compartment capacity, and evaluates its performance in four scenarios. The results demonstrate proposed approach efficiently obtains high-quality joint reservation plans, resulting in a notable reduction of approximately 20% in platform delay time and 50% in off-platform queuing time in suitable scenarios. The paper also discusses the implementation challenges and limitations, and provides recommendations for effective application.

Exploring the Spatiotemporal Effects of the Built Environment on the Nonlinear Impacts of Metro Ridership: Evidence from Xi’an, China

Exploring the correlation of the built environment with metro ridership is vital for fostering sustainable urban growth. Although the research conducted in the past has explored how ridership is nonlinearly influenced by the built environment, less research has focused on the spatiotemporal ramifications of these nonlinear effects. In this study, density, diversity, distance, destination, and design parameters are utilized to depict the “5D” traits of the built environment, while Shapley Additive Explanations with eXtreme Gradient Boosting (XGBoost-SHAP) are adopted to uncover the spatial and temporal features concerning the nonlinear relationship of the built environment with ridership for metro stations located in Xi’an. We conducted a K-means clustering analysis to detect different site clusters by utilizing local SHAP coefficients. The results show that (1) built environment variables significantly influence metro ridership in a nonlinear manner at different periods and thresholds, with the POI facility density being the most critical variable and the other variables demonstrating time-driven effects; (2) the variables of population density and parking lot density exhibit spatial impact heterogeneity, while the number of parks and squares do not present a clear pattern; and (3) based on the clustering results, the metro stations are divided into four categories, and differentiated guidance strategies and planning objectives are proposed. Moreover, the current work offers a more developed insight into the spatiotemporal influence of built environments on metro travel in Xi’an, China, using nonlinear modeling, which has vital implications for coordinated urban–metro development.

Exposure to PM2.5 on Public Transport: Guidance for Field Measurements with Low-Cost Sensors

Air pollution is one of the most important problems in big cities, resulting in adverse health effects. The aim of the present study was to characterize the personal exposure to indoor and outdoor pollution in the Greater Athens Area in Greece by taking measurements during a journey from suburban to mixed industrial–urban areas, encompassing walking, waiting, bus travel, and metro travel at various depths. For this reason, low-cost (LC) sensors were used, and the inhaled dose of particulate matter with an aerodynamic diameter of less than or equal to 2.5 μm (PM2.5) in different age groups of passengers was calculated. Specific bus routes and the Athens metro network were monitored throughout different hours of the day. Then, the average particulate matter (PM2.5) exposure for a metro passenger was calculated and evaluated. By considering the ventilation rate of a passenger, an estimation of the total PM2.5 inhaled dose for males and females as well as for different age groups was made. The results showed that the highest PM2.5 concentrations were observed inside the wagons with significant increases during rush hours or after rush hours. Furthermore, there should be a concern regarding older individuals using the subway network in Athens during rush hours and in general for sensitive groups (people with asthma, respiratory and cardiovascular problems, etc.).

Understanding post-pandemic spatiotemporal differences in the recovery of metro travel behavior among different groups by considering the built environment

Numerous studies have substantiated the substantial impact of COVID-19 on metro travel, which is expected to gradually recover once the pandemic is controlled. Given the potentially more severe repercussions of COVID-19 on vulnerable groups like the elderly and people with disabilities, recovery patterns may differ significantly among various demographic segments. However, limited research has addressed this notable disparity. To address this gap, we collected metro travel data in Wuhan from March 2019 to April 2021. We analyzed changes in travel characteristics among different groups, such as the elderly, people with disabilities, commuters, school students, and others, before and after the pandemic. By employing interrupted time series analysis, we explored the short-term impact of the pandemic on different groups and their long-term recovery trajectories. We also investigated the factors influencing the recovery of metro travel among diverse demographic groups. The findings indicate the following: (1) All groups experienced a sharp decline in travel ridership and frequency in the short term due to the pandemic. (2) There are distinct variations in long-term ridership recovery among different groups, with commuters and school students showing the quickest recovery. However, ridership among people with disabilities remained below pre-pandemic levels even a year after the pandemic. (3) Given the inherent spatiotemporal regularity in residents’ daily activities, post-pandemic metro travel patterns closely align with the pre-pandemic patterns. (4) Different built environment factors exert varying degrees of influence on the recovery of metro ridership among different groups, and distinctions are evident between weekdays and weekends. These findings enhance our comprehension of the pandemic’s impact on diverse demographic groups, which can guide government agencies and urban planners in formulating more resilient strategies for rail transit operations and land use optimization.

Identification and Spatiotemporal Analysis of Bikesharing-Metro Integration Cycling

An essential function of dockless bikesharing (DBs) is to serve as a feeder mode to the metro. Optimizing the integration between DBs and the metro is of great significance for improving metro travel efficiency. However, the research on DBs–Metro Integration Cycling (DBsMIC) faces challenges such as insufficient methods for identification and low identification accuracy. In this study, we improve the enhanced two-step floating catchment area and incorporate Bayes’ rule to propose a method to identify DBsMIC by considering the parameters of time, distance, environmental competition ratio, and POI service power index. Furthermore, an empirical study is conducted in Shenzhen to verify the higher accuracy of the proposed method. Their spatiotemporal behavior pattern is also explored with the help of the kernel density estimation method. The research results will help managers improve the effective redistribution of bicycles, promote the coupling efficiency between transportation modes, and achieve sustainable development of urban transportation.

Metro travel and perceived COVID-19 infection risks: A case study of Hong Kong

The COVID-19 pandemic has exerted unprecedented impacts on travel behaviors because of people's increased health precautions and the presence of various COVID-19 containment measures. However, little research has explored whether and how people changed their travel with respect to their perceived local infection risks across space and time. In this article, we relate elasticity and resilience thinking to the changes in metro travel and perceived infection risks at the station or community level over time. Using empirical data from Hong Kong, we measure a metro station's elasticity as the ratio of changes in its average trip length to the COVID-19 cases' footprints around that station. We regard those footprints as a proxy for people's perceived infection risks when making trips to that station. To explore influencing factors on travel in the ups and downs of perceived infection risks, we classify stations based on their elasticity values and examine the association between stations' elasticities and characteristics of stations and their served communities. The findings show that stations varied in elasticity values across space and different surges of the local pandemic. The elasticity of stations can be predicted by socio-demographics and physical attributes of station areas. Stations serving a larger percentage of population with higher education degrees and certain occupations observed more pronounced trip length decrease for the same level of perceived infection risks. The number of parking spaces and retail facilities significantly explained variations in stations' elasticity. The results provide references on crisis management and resilience improvement amid and post COVID-19.

Multiscale spatial analysis of metro usage and its determinants for sustainable urban development in Shenzhen, China

Metro systems play a critical role in contemporary urban development. With proper planning and deployment, metro systems can often lead to enormous socioeconomic and environmental benefits. As such, a bulk of studies have been conducted to investigate metro usage determinates for more efficiently harvesting its benefits. However, past studies are found with three inherent drawbacks, including incapability of capturing multiscale features of different covariates among commonly adopted models, overlooking socioeconomic ingredients (e.g., housing prices and fine-grained population density), and neglecting temporal variations, thus likely leading to biased findings. This study aims to understand metro usage determinants in four divided time slots, i.e., morning peak, off-peak (day), evening peak, and off-peak (night) in Shenzhen, China by applying multiple datasets into a novel model of multiple geographically weighted regression (MGWR). The findings indicate that (1) the built environment factors, socioeconomic elements, and intermodal connection factors influence metro usage in different manners; (2) compared with the other four traditional models, MGWR demonstrates the best performance in explaining metro usage determinants; (3) different important variables affecting metro ridership are identified with the Random Forest technique for all the four time periods in Shenzhen and (4) the correlation between explanatory variables and metro usage is found varying with different spatial patterns. This study is not only of methodological value to firstly apply MGWR to the domain of metro usage but also of practical value to inform decision-makers on how to better promote and predict metro travel demand and ultimately achieve low-carbon and sustainable urban development visions.

DEVELOPMENT OF THE TRAVEL SATISFACTION SCALE (TSS) FOR THE ASSESSMENT OF COMMUTERS’ SATISFACTION IN PUBLIC TRANSPORT: EVIDENCE FROM DELHI METRO (INDIA)

The Travel Satisfaction Scale (TSS) was created to gauge public opinion on Delhi Metro travel. It has two affective dimensions and one cognitive dimension. This study leverages data from the Delhi Metro commuter trips to undertake new tests because there has been little research on its reliability and structure in the past. Differences in the TSS's reliability and structure – notably for the Delhi Metro and the demographics of the region – are also considered. Finally, the outcomes of this study imply that a single dimension of the affective dimension, rather than the two sub-dimensions, provides a better fit for the Delhi Metro, as well as other public transportation infrastructures in developing countries like India. Individual objects do not load on the two emotional dimensions as intended in a three-dimensional structure, which is more suited for public transportation. Two of the scale's elements – enthusiastic/bored and relaxed/hurried – were associated with the other items in a previous study differing from ours. Researchers should adapt the structure of the TSS in the future by adding or replacing some items with alternate options, which will make it easier to collect data and reduce the burden on the respondent, as well as increase the reliability of the data while maintaining the TSS's consistency and balance.

Hybrid Scheduling Model Based on Fare Incentives for Peak Time Interval of the Metro: The Harbin Metro System

Considering the operation efficiency of the metro system, the boarding queuing problem is a complex and intractable dilemma caused by the security and ticket checking process in urban metro stations, especially for the mass-transit metro system in China. In this study, a novel scheme of fare differentials based on demand control and congestion management for reducing congestion in the metro boarding process was proposed. In addition, we extended Vickrey’s point-queue model into a boarding congestion model incorporating the bidesired departure time considering the security and ticket checking process in stations as a continuum bottleneck. The train-run and departure time choosing behaviors of passengers under different schedule gaps of the adjacent train runs are explored when the boarding system achieves user equilibrium in the proposed model. Then, we examined the demand regulatory mechanisms of fare incentives (fare differentials) in reducing the queuing boarding time in metro stations when the optimization of the schedule gaps is ineffective in the pattern of mass-scale travel demand. The analytical solutions of these two optimal methods (schedule gaps and fare differentials) for boarding congestion management are presented. After comparing the two congestion-reduced methods, the fare incentive rule has a better regulation effect on the smoothing of travel demand. The results of the sensitivity analysis using numerical simulations reveal the regulatory mechanism of fare differentials in reducing the queuing time and increasing the incremental revenue. (1) The demand threshold is only related to the boarding capacity and schedule gaps (i.e., the greater the boarding capacity and schedule gaps, the greater the passenger capacity of the metro station). (2) The effect of fare incentives in reducing the boarding congestion is better if the lower fare is implemented in the later train runs. (3) A lower fare differential between two adjacent metro runs can be used to regulate the proportion of staggered passengers in the queuing line to reduce the crowd gathering in the metro station hall when travel demand is high, meanwhile, a higher fare differential between two adjacent metro shuttles can increase incremental revenue effectively. (4) The measure of fare differentials causes worse results in both the reduction of the queuing time and the increase of the incremental revenue when the metro travel demand is lower than the demand threshold. This conclusion is consistent with the pattern of reality and experience. Therefore, the definition and judgment conditions of demand thresholds, introduced in this study, can provide theoretical guidance when implementing fare incentive policies in aviation and metro networks.

How Does Built Environment Affect Metro Trip Time of Elderly? Evidence from Smart Card Data in Nanjing

Understanding the determinants of elderly people’s public transport usage patterns can offer new insights into elderly mobility issues and provide policy implications for planning toward an aging-friendly and sustainable city. However, few studies have examined the impact of the built environment on the trip time of the elderly using big data. Moreover, the elderly’s trip time has been mostly investigated by the multivariate linear regression model (MLR), ignoring the non-linear association between explanatory variables and trip time. Using smart card data from Nanjing in 2019, this study employs a gradient boosting regression trees (GBRT) model to probe into the correlations between the built environment and the elderly’s trip time. The results show that significant non-linear relationships exist between trip time and the selected explanatory variables, which cannot be captured by the MLR model. It suggests that relevant policy implementations should be carried out in conjunction with the elderly’s travel environment by regarding their threshold effects. Besides, interaction effects of spatial attributes on trip time are identified in our study. For example, elderly people living in the exurban area are more likely to take long-distance metro travel for their physical exercise. These findings demonstrate that planners and policymakers should not only consider one single built-environment factor, but also the interactions of various factors to enhance elderly mobility.

An alternative method for assessing urban transportation carrying capacity

Urban transportation is a complex system typically composed of car, bus, non-motorized, and metro travel modes, which connects various residential traveling and transport facilities. It is important to have a proper method to facilitate understanding on urban transportation carrying capacity (UTCC) of individual types of travel modes and the balance performance between the carrying capacities offered by different travel modes. This study introduces an alternative model named “load-carrier” perspective method for assessing the four types of UTCC. A measurement of coordination degree is used to measure if the carrying capacities between different types of travel modes are developed and utilized in balance. An application of the proposed method is conducted by using empirical data collected from 805 traffic analysis zones (TAZs) in Chongqing City in China in 2020. The research findings suggest that: 1) the UTCC performance can be divided into five grades, namely, low, mild, moderate, high, and extremely high level of utilization intensity. 2) most of the TAZs in Chongqing were in low and mild level of utilization intensity of UTCC. 3) the balance performance between four types of transportation carrying capacities was poor in Chongqing City, with 97.1% of the sample TAZs are in imbalanced and slight imbalanced status. The demonstration conducted proves that the introduced method provides an important tool for examining the UTCC performance by different travel modes.

Metro passenger flow forecasting though multi-source time-series fusion: An ensemble deep learning approach

The increasing free access of the Internet provides us with favorable circumstances to investigate search engine index reflecting more and more personal behavior information. Part of valuable travel search information can assist us to achieve more robust and reliable prediction of metro passenger flow. Inspired by this, the paper develops a new multi-source time series fusion and direct interval prediction approach to grasp the dynamic law of metro passenger flow effectively. Multi-source index regarding metro travel from three major search engines (Baidu, Sogou and 360) in China are screened out and fused into the powerful predictors. By integrating an optimized multivariate mode decomposition strategy and long short-term memory model, lower and upper bounds of prediction interval are estimated directly by a multi-objective framework that combines the advantages of both the deep learning models long short-term memory and the ensemble learning approach. Especially, two sets of real experiment data of Beijing and Shanghai metro systems are employed to test our approach. Findings show that fusion of multi-source index information promotes the predictability of metro passenger flow, contributing to improving operation management and service quality.

Helping Hand: Metro Travel Assistance App for Elderly and Differently Abled People

Helping Hand: Metro Travel Assistance App for Elderly and Differently Abled People

Inter-city transport infrastructure and intra-city housing markets: Estimating the redistribution effect of high-speed rail in Shenzhen, China

This study analyses the changes in intra-city housing values in response to improved inter-city connection brought by high-speed rail (HSR), using the opening of the Hangzhou–Fuzhou–Shenzhen Passenger Dedicated Line (HFSL) in Shenzhen, China, as an example. The opening of the HFSL and its integration into the local metro network at Shenzhen North Station provide exogenous intra-city variations in access to the surrounding economic mass. With a difference-in-differences approach, we find that the HFSL showed a negative local effect as housing values declined by 11.5%–13.3% in the proximity of Shenzhen North Station relative to areas further from the station after the opening, possibly due to the negative externalities of the HFSL. The HFSL effect can spread along the metro network and lead to, on average, a 7% appreciation of housing values around metro stations (network effect). The direction and strength of the network effect vary by metro travel time between Shenzhen North Station and metro stations. Housing values decreased by 7.7% around metro stations within 5–15 minutes of metro travel time but increased by 63.6%, 16.6% and 29.2% around metro stations within 15–25, 25–35 and 35–45 minutes of metro travel time to Shenzhen North Station, respectively. The HFSL effect on housing values diminishes when the rail travel time is above 45 minutes. We interpret these findings as evidence of the redistribution effect in the city related to HSR connection.

Inter-city Transport Infrastructure and Intra-city Housing Markets: Estimating the Redistribution Effect of High-Speed Rail in Shenzhen, China

This study analyzes the changes in intra-city housing values in response to improved inter-city connection brought by high speed rail (HSR), using the opening of the Hangzhou-FuZhou-Shenzhen Passenger Dedicated Line (HFSL) in Shenzhen, China as an example. The opening of the HFSL and its integration into the local metro network at Shenzhen North Station provide exogenous intra-city variations in access to surrounding economic mass. With a difference-in-differences approach, we find that the HFSL showed a negative local effect as housing values declined by 11.5-13.3% in the proximity of Shenzhen North Station relative to areas further from the station after the opening, possibly due to the negative externalities of the HFSL. The HFSL effect can spread along the metro network and lead to, on average, a 7% appreciation of housing values around metro stations (network effect). The direction and strength of the network effect vary by metro travel time between Shenzhen North Station and metro stations. Housing values decreased by 7.7% around metro stations within 5-15 minutes of metro travel time but increased by 63.6%, 16.6%, and 29.2% around metro station within 15-25, 25-35, and 35-45 minutes of metro travel time to Shenzhen North Station, respectively. The HFSL effect on housing values diminished when the travel time is above 45 minutes. We interpret these findings as evidence of redistribution effect in the city related to HSR connection.