Relationship of Walk Access Distance to Rapid Rail Transit Stations with Personal Characteristics and Station Context

Towards a collective opinion generation approach with multiple objectives for evaluating rail transit station accessibility in urban areas

Forecasting daily and weekly passenger demand is a key fundamental process used by existing urban rail transit (URT) station authorities to diagnose operational problems and make decisions about train schedule patterns to improve operational efficiency, increase revenue management, and improve driving safety. The accuracy of the forecast results will directly affect the operation planning of urban rail transit (URT). Therefore, based on the collected inbound historical passenger data, this study used the Box–Jenkins time series with the Facebook Prophet algorithm to analyze the characteristics of urban rail transit passenger demand and achieved better computational forecasting performance accuracy. After analyzing the periodicity, correlation, and stationarity, different time series models were constructed. The Akaike information criteria (AIC), Bayesian information criteria (BIC), mean squared error (MSE), and root mean squared error (RMSE) were used to evaluate the adequacy of the best forecast model from among several tested candidates’ models for the Box–Jenkins. The parameters of the daily and weekly models were estimated using statistical software. The experimental results of this study are of both theoretical and practical significance to the urban rail transit (URT) station authorities for an effective station planning system. The forecasting results signify that the SARIMA (5, 1, 3) (1, 0, 0)24 model performs better and is more stable in forecasting the daily passenger demand, and the ARMA (2, 1) model performs better in forecasting the weekly passenger demand. When comparing the SARIMA and ARMA models with the Facebook Prophet, results show that the Facebook Prophet model is superior to the SARIMA model for the daily time series, and the ARMA model is superior to the Facebook Prophet model for the weekly time series.

Urban rail transit stations, characterized by high passenger density and confined spaces, face significant challenges in emergency evacuations during peak hours. To address the complexity of station layouts and crowd dynamics, this study introduces a reinforcement learning–enhanced framework for simulating passenger flow dynamics in urban rail transit (URT) stations, integrating Wi-Fi probe data with Markov decision process (MDP)-driven path planning. By formulating pedestrian navigation as an MDP and optimizing decision policies through a dueling double deep Q-Network (D3QN), the proposed method addresses the limitations of traditional A* algorithms in handling complex crowd behaviors and dynamic congestion patterns. Wi-Fi probes deployed across station infrastructure capture real-time passenger trajectories, enabling data-driven calibration of the MDP state space and reward function. The D3QN architecture leverages prioritized experience replay and adaptive exploration to balance path optimality with obstacle avoidance, achieving a 92.4% path similarity index (PSI) to ground-truth trajectories. Experimental validation at Shenzhen Children’s Palace Station in China demonstrated a 20.3% improvement in evacuation efficiency and a 5.9% evacuation time error (ETE), outperforming traditional A* and social force models. The framework identifies critical congestion zones at fare gate intersections and escalator merges, reducing peak densities by 23.7% through anticipative rerouting. This work establishes a scalable paradigm for real-time crowd management in transportation hubs, bridging data-driven sensing with AI-optimized simulation.

The study on mass performance in Urban Rail Transit (URT) stations helps to enhance efficiency and security of passenger service, and supports design and amelioration of stations. The procedure of Mass Service Routing and Assignment (MSRA) is similar to the routing of ant colonies looking for food. This paper analyzes the service network of URT stations and the hierarchical structure of the mass, and brings forward Dynamic Multiple Ant Colony Optimization (DMACO) to study MSRD. In DMACO, the mass is divided into colonies corresponding with passenger flows, and original Ant Colony Optimization is used in all the flows simultaneously. The effects, from other colonies, guidance, capacity of network, and preference of ants to pheromone, are imported in state transition rules, in order to meet MSRA in condition of multi-hierarchy, multi-process passenger service in a URT station.

Qualified with benefits in the aspects of environment, economy, and society, urban rail transit presents a rapid development in China during recent decades. Increasing initiatives and practices with views to facilitate people's lives and intensively utilize land resources have been engaged. Accordingly, urban rail transit stations with multi-layer structure integrated in three-dimensional space have been designed and built in many cities. Due to the complexity of the rail transit stations, there is a need to identify those high-risk nodes in the stations and develop relevant risk management strategies. Therefore, this paper introduces a new approach to identify high-risk nodes in the urban rail transit stations through field investigation and complex network analysis. The Chongqing Lianglukou rail transit station was selected for case study. With the determined high-risk nodes, rail transit management firms can develop relevant risk mitigation measures for those high-risk nodes, re-allocate their resources to create a safe and secure environment for passengers in urban rail transit stations.

Metro Manila’s traffic congestion problem had been a concern for the travel behavior of commuters due to the lack of mobility and difficulty in accessibility. A shift from automobile to transit use would be beneficial for the progress of a transit-oriented development in Metro Manila. Using questionnaire survey and logistic regression analysis, the effects of different socio-economic and land use characteristics among commuters using urban rail transit and automobile, and whose residences are along stations of the rail transit would determine the behavior of their travel. The research outcomes entails that commuters whose residents are very near the rail transit stations are more likely to utilize them. On the other hand, as the residence to transit station proximity increases, a higher likelihood of choosing automobiles or different public transit as their modes of transport was observed, but due to some beneficial factors and own perception, some commuters with the same land use characteristic, still use rail transit.

The emergence of dockless shared bikes (DSB) has led to their use as an important transfer mode to urban rail transit (URT) stations. However, in highly populated areas such as subway stations in peak hours, there is increasing concern about the imbalance between the demand and supply of shared bikes. To promote smoother subway transfer trips using shared bikes, it is very important to estimate the DSB demand, especially the disparity in the volume of bike pick-up and drop-off demand around subway stations. This research first utilizes the Shenzhen metro usage data and DSB usage data, analyzes data regarding subway and shared bike usage, discusses their potential transfer uses, and finds great disparity in DSB demand between different subway stations. The catchment area method is used to estimate bike usage as a potential transfer mode to the subway, where the catchment area is defined as a radius of 150 m from the subway station center. The DSB trip demand is categorized into two types: pick-up and drop-off. The most recent deep learning method, adaptive graph convolutional recurrent network (AGCRN), is used to predict the DSB demand more accurately because of its ability in enabling the modeling of relationships between entities in a self-adapted graph, and the prediction is compared with long short-term memory (LSTM), spatiotemporal neural network (STNN), diffusion convolutional recurrent neural network (DCRNN), and Graph WaveNet. Results show that methods with graphs (STNN, DCRNN, Graph WaveNet, and AGCRN) perform better than LSTM, and methods with adaptive graphs (Graph WaveNet and AGCRN) outperform methods with static graphs in terms of mean absolute error (MAE), root-mean-square error (RMSE), and mean absolute percentage error (MAPE). DSB prediction results show that AGCRN performs the best in this study. More data, particularly land use data and URT station volume data, are expected to improve the predictive accuracy of the method due to potentially improved graph representation of station characteristics and subway station volume correlations. And with more accurate prediction results, it will be possible to achieve a better balancing strategy for bike operation optimization for better bike usage, and thus for a higher transfer rate of DSB to subway.

The spatial and temporal rules governing passenger flow in urban rail transit (URT) stations are complex, and simulation modeling and analysis of passenger flow distribution in stations are very important in regard to scientifically organizing and controlling passenger flow and improving passenger travel efficiency. With a focus on the multilevel three‐dimensional spatial structure of URT stations and the composition of multiclass passenger flow lines, the travel process and microbehavior of passengers are analyzed here. The goal‐driven behavior of passenger flow groups in the free area and the interaction between them are considered, and a static–dynamic field hybrid model describing the differences in speed between passengers, their walking, and avoidance behavior and a queue field model of queuing behavior are constructed. A selection behavior model for facility nodes such as gates, interlayer facilities, and waiting areas is constructed to represent heterogeneous passenger flow to multiservice channels. A passenger flow simulation method framework for URT stations that takes into account heterogeneous passenger flow, the 3D spatial structure, and multipotential energy field is also established. The effectiveness of the proposed model and method is verified via simulation of Changsha Metro Shumuling Station, and it is found that the proportion of escalators selected as interlayer facilities is significantly higher than that for stairs. After a train leaves the station, the passenger flow density on both sides of the platform reaches more than 1.5 person/m2, significantly higher than that in the central area of the platform. The average passing times for passengers at the exit gate and the ascending escalator are 16–18 and 13–14 s, respectively. The average queue length and passing times for passengers are higher than those at the entrance gate and the descending escalator. These results can provide support for decisions on the actual operation of URT stations.

Over the past three decades, Transit-Oriented Development (TOD), with transit as its central tenet, has emerged as a pivotal urban policy driving sustainable and intelligent urban growth, drawing significant attention from researchers and practitioners worldwide. TOD involves creating high-density, mixed-use, pedestrian-friendly urban areas around transit stations to enhance transit accessibility, promote social cohesion, and improve housing conditions. However, the global implementation of TOD has encountered challenges across various domains including transportation, housing, and employment, thereby exacerbating inequities within the built environment. This study adopts a TOD perspective to comprehensively review the equity impacts of urban rail transit (URT) station areas on the built environment, with a particular focus on social, travel, perception, health, and spatial dimensions, and their impacts on promoting or hindering equitable outcomes among diverse societal groups. Utilizing a scoping review methodology, the study encapsulates the progress and themes in the field, employing a systematic approach to meticulously analyze the outcomes of each research theme. The findings reveal that URT station areas have positive impacts on economic growth and property values. However, they can also contribute to gentrification, exacerbating disparities between different societal groups in station and non-station areas, along with an unequal distribution of resources and opportunities. Additionally, while these station areas encourage pedestrian activity and public transportation usage, they also carry the potential for environmental pollution, raising concerns about spatial accessibility and facility convenience, thereby impacting environmental equity. This study employs comprehensive and critical theoretical analyses, utilizing intricate methods and detailed indicators, to elucidate disparities in equity outcomes of URT station areas across different societal groups. The crucial challenge in future research lies in integrating the concept of equity into TOD planning strategies. This study aims to provide standardized and harmonized criteria for guiding equitable TOD planning policies, thereby enhancing the scientific basis and effectiveness of planning strategies. Ultimately, it seeks to offer theoretical insights towards the creation of a more equitable and inclusive urban built environment in the future.

Better understanding the choice of travel mode by urban residents: New insights from the catchment areas of rail transit stations

Urban rail transit is an essential part of the urban public transportation system. The reasonable spatial data visualization of urban rail transit stations can provide a more intuitive way for the majority of travelers to arrange travel plans and find destinations. The map service of rail transit stations generated by data visualization has gradually become indispensable information guidance in the rail transit system. The existing map service icons block each other when the scale changes, and new stations cannot be displayed dynamically when users drag the map. This paper uses filtering and sorting methods to dynamically query and visualize the relatively more important transportation stations within the users’ visible range, so as to solve the above problems and provide people with better transportation services. Our method introduces three constraints: spatial diversity, time-sharing passenger flow analysis and whether it is a transit station, and calculates the scores of constraint relationships of feature objects to evaluate stations. On the basis of the skyline query, the scores of feature objects are combined and sorted to obtain an ordered object set of the most interesting k points(top-k POIs), and the rail transit stations are dynamically retrieved and visualized. Before sorting POIs, we filter out POIs that need to be fitted, so that only the k most representative POIs in the currently visible range are displayed. When the map scale changes, the displayed POIs are updated. Finally, through the statistics of efficiency calculation of this method under different scales and centers, combined with users’ evaluations, it was proved that our method could better display critical information and improve user experience.

As fundamental nodal elements in urban spatial structures, the coupling and coordinated development of urban business centers and urban rail transit contributes to the optimization of these structures. Utilizing complex network theory, a model for the urban rail transit network was constructed. The importance and hub nature of urban rail transit stations were evaluated from different angles, including degree centrality, closeness centrality, betweenness centrality, and eigenvector centrality. These metrics examined the station's degree, closeness to other nodes, number of shortest paths, and centrality of neighboring nodes. The coupling relationship between urban rail transit and urban business centers was taken into account, leading to the creation of a coupling and coordination degree model for urban rail transit stations and urban business centers. An analysis of the spatio-temporal evolution of the coupling relationship between urban rail transit and business centers in Beijing, Shanghai, and Guangzhou from 2000 to 2020 was conducted. The findings indicated an interactive and mutually influencing coupling relationship between the urban rail transit network and urban business centers. Over time, the coupling and coordination degree of urban rail transit stations and urban business centers trended from being uncoordinated towards preliminary, moderate, and good coordination. Spatial heterogeneity existed in the coupling and coordination status of different circles, with the best coupling and coordination conditions being in the core area. There was a degree of variance in the coupling and coordination development situation of rail transit stations and business centers in the core areas of different cities. Among them, Shanghai's core area had the best spatial coupling and coordination development situation, Beijing's core area lagged in business center development compared to the construction of the urban rail transit network, while Guangzhou's core area saw urban rail transit network development lag behind its mature business centers. The application of these research findings aids in promoting sustainable urban development. While this study primarily measured the importance of urban rail transit network stations from the node centrality perspective, future studies could further examine the spatial coupling of urban rail transit and business centers from the viewpoints of accessibility and passenger flow.

With the rapid expansion of urban rail transit, station service quality has become an important issue for improving overall operational efficiency and passenger travel experience. This study focuses on the service quality issues of rail transit stations during peak hours, analyzing factors such as passenger flow congestion, inadequate facility capacity, delayed information dissemination, and security management. Through both quantitative and qualitative analysis, the study identifies weak points in station services and proposes multi-dimensional improvement strategies, including passenger flow diversion, facility optimization, introduction of intelligent devices, and upgrades to the information release system. The research results indicate that implementing these optimization measures can significantly improve station throughput efficiency, reduce passenger waiting times, and enhance the overall passenger travel experience. Furthermore, this study provides valuable experience for other urban rail transit stations and contributes to the sustainable development of rail transit systems.

<div>To identify the influences of various built environment factors on ridership at urban rail transit stations, a case study was conducted on the Changsha Metro. First, spatial and temporal distributions of the station-level AM peak and PM peak boarding ridership are analyzed. The Moran’s I test indicates that both of them show significant spatial correlations. Then, the pedestrian catchment area of each metro station is delineated using the Thiessen polygon method with an 800-m radius. The built environment factors within each pedestrian catchment area, involving population and employment, land use, accessibility, and station attributes, are collected. Finally, the mixed geographically weighted regression models are constructed to quantitatively identify the effects of these built environment factors on the AM and PM peak ridership, respectively. The estimation results indicate that population density and employment density have significant but opposite influences on the AM and PM peak ridership, which can be attributed to the opposite travel directions of commuters in the AM and PM peak. The recreational facility density, road density, and 10-min walking accessibility could significantly positively affect the PM peak ridership, and their influences vary greatly over space. Besides, the operating time of stations significantly positively affects both the AM and PM peak ridership, transfer stations have significantly larger PM peak ridership and terminal stations have significantly larger AM peak ridership. The findings are expected to provide new insights for agencies to formulate appropriate measures to improve the ridership of urban rail transit.</div>

With the fast development of urban rail transit in China, the comprehensive use and development of the land surrounding rail transit stations become an important development strategy for all big cities. How to select “nodes”, which represent those stations, with a significant development value in a rail transit network is the key component of comprehensive land use and development. In order to quantitatively assess the value of comprehensive land use and development for urban rail transit stations, this paper demonstrates a method to assess this value by evaluating the accessibility ofnodes in the rail transit network and conducts a case study of simulation analysis on the existing rail transit network in Chengdu by using ArcGIS. The results of the simulation show that the rail transit stations with more transfer routes have better accessibility and the land around these stations is of higher value for comprehensive utilization and development. In addition, this paper found that the accessibility of rail transit stations should be improved with more rail transit express lines to connect to stations, and rail transit loop lines have better accessibility of stations than non-loop lines.