Subway Network Research Articles

Similarity and economy of scale in urban transportation networks and optimal transport-based infrastructures

Designing and optimizing the structure of urban transportation networks is a challenging task. In this study, we propose a method inspired by optimal transport theory and the principle of economy of scale that uses little information in input to generate structures that are similar to those of public transportation networks. Contrarily to standard approaches, it does not assume any initial backbone network infrastructure but rather extracts this directly from a continuous space using only a few origin and destination points, generating networks from scratch. Analyzing a set of urban train, tram and subway networks, we find a noteworthy degree of similarity in several of the studied cases between simulated and real infrastructures. By tuning one parameter, our method can simulate a range of different subway, tram and train networks that can be further used to suggest possible improvements in terms of relevant transportation properties. Outputs of our algorithm provide naturally a principled quantitative measure of similarity between two networks that can be used to automatize the selection of similar simulated networks.

Resilience Measurement of Bus–Subway Network Based on Generalized Cost

Resilience Measurement of Bus–Subway Network Based on Generalized Cost

Nonlinear Relationship of Multi-Source Land Use Features with Temporal Travel Distances at Subway Station Level: Empirical Study from Xi’an City

The operation of the subway system necessitates a comprehensive understanding of passenger flow characteristics at station locations, as well as a keen awareness of the average travel distances at these stations. Moreover, the travel distances at the station level bear a direct relationship with the built environment composed of land use characteristics within the station’s catchment area. To this end, we selected the land use features within an 800 m radius of the station (land use area, distribution of points of interest, and the surrounding living environment) as the influencing factors, with the travel distances at peak hours on the subway network in Xi’an as the research subject. An improved SSA-XGBOOST-SHAP interpretable machine learning framework was established. The research findings demonstrate that the proposed enhanced model outperforms traditional machine learning or linear regression methods in terms of R-squared, MAE, and RMSE. Furthermore, the distance from the city center, road network density, the number of public transit routes, and the land use mix have a pronounced influence on travel distances, reflecting the significant impact that mature built environments can have on passenger attraction. Additionally, the analysis reveals a notable nonlinear relationship and threshold effect between the built environment variables comprising land use and the travel distances during peak hours. The research results provide data-driven support for operational strategy management and line capacity optimization, as well as theoretical underpinnings for enhancing the efficiency and sustainability of the entire subway system.

Unacknowledged health costs of subway transportation: Commuting time dominant environmental inequality in PM2.5 exposure

IntroductionThe subway network offers economical and swift transportation for long-distance commuters, simultaneously mitigating urban congestion and environmental challenges in sprawling metropolises. Nonetheless, studies have revealed that subway represent a microenvironment with heightened exposure to fine particulate matter (PM2.5), a facet of air pollution overlooked by the majority of commuters. Focusing on the case of Beijing's subway commuters, we assess the health and economic impact of personal exposure to PM2.5 during their commuting, as well as the distributional effects of these impacts across subgroups of populations with different daily commuting durations. MethodThis study adopts an integrated process to estimate the health outcomes and associated economic loss caused by subway commutes under different exposure scenarios. The overall daily exposure was estimated by aggregating exposure in different microenvironments using the microenvironmental exposure model. The health impact was then evaluated using the integrated exposure-response (IER) model. The economic value of the health impact was assessed based on the value of statistical life (VSL) of the affected population. ResultOur findings indicate that subway commutes lead to an increase of 11.6% and 26.3% in health damage and corresponding economic loss attributable to PM2.5 exposure through “crowding out” of exposure duration in relatively “cleaner” microenvironments in 2017 and 2020, respectively. Long-distance commuters who travel over 100 min per day, make up 25% of subway commuters and are disproportionately responsible for 40% of the total health damages and economic losses caused by subway PM2.5 exposure. Reducing PM2.5 concentration in subway cabins can more effectively offset the additional impacts brought about by the extended and rather inelastic subway commuting. ConclusionThe result unearthed by our study advocates for urgent measures to ameliorate the air quality within subway systems, a move that promises to mitigate the unequal burdens and rectify the inequities borne by at-risk demographics.

The subway and the gender wage gap

We estimate the impacts of accessing the subway network on the gender wage gap in China. The results suggest that the subway increases the wages of females relative to males. Heterogeneity estimations indicate that the subway has a more significant impact in regions with mature subway networks, as well as in suburban areas. The findings also demonstrate that the subway reduces the gender wage gap among low-skilled groups, married households, and households with fewer vehicles. Analysis of the mechanisms reveals that the effects are driven by the labour demand side, as subways boosts the entry of firms that are intensive in employing low-skilled females.

Emergence of Complex Network Topologies from Flow-Weighted Optimization of Network Efficiency

Transportation and distribution networks are a class of spatial networks that have been of interest in recent years. These networks are often characterized by the presence of complex structures such as central loops paired with peripheral branches, which can appear both in natural and manmade systems, such as subway and railway networks. In this study, we investigate the conditions for the emergence of these nontrivial topological structures in the context of human transportation in cities. We propose a simple model for spatial networks generation, where a network lattice acts as a planar substrate and edge speeds define an effective temporal distance which we aim to optimize and quantifies the efficiency in exploring the urban space. Complex network topologies can be recovered from the optimization of edges’ speeds and we study how the interplay between a flow probability between two nodes in space and the associated travel cost influences the resulting optimal network. In the perspective of urban transportation we simulate these flows by means of human mobility models to obtain origin-destination matrices. We find that when using simple lattices, the obtained optimal topologies transition from treelike structures to more regular networks, depending on the spatial range of flows. Remarkably, we find that branches paired to large loops structures appear as optimal structures when the network is optimized for an interplay between heterogeneous mobility patterns of small range travels and longer-range ones typical of commuting. Moreover, when congestion dynamics in traffic routing is considered, we study the emergence of additional edges from the tree structure to mitigate temporal delays. Finally, we show that our framework is able to recover the statistical spatial properties of the Greater London area subway network. Published by the American Physical Society 2024

Urban Traffic Dominance: A Dynamic Assessment Using Multi-Source Data in Shanghai

This study redefines the evaluation of urban traffic dominance by integrating complex network theory with multi-source spatiotemporal trajectory data, addressing the dynamic nature of various transportation modes, including public transit and shared mobility. Traditional traffic studies, which focus predominantly on static road traffic characteristics, overlook the fluid dynamics integral to urban transport systems. We introduce Relative Weighted Centrality (RWC) as a novel metric for quantifying dynamic traffic dominance, combining it with traditional static metrics to forge a comprehensive traffic dominance evaluation system. The results show the following: (1) Both static and dynamic traffic dominance display core-periphery structures centered around Huangpu District. (2) Dynamically, distinct variations in RWC emerge across different times and transport modes; during the early hours (0:00–6:00), shared bicycles show unique spatial distributions, the subway network experiences a notable decrease in RWC yet maintains its spatial pattern, and taxis exhibit intermediate characteristics. Conversely, the RWC for all modes generally increases during morning (6:00–12:00) and evening (18:00–24:00) peaks, with a pronounced decrease in subway RWC in the latter period. (3) The integration of dynamic evaluations significantly modifies conventional static results, emphasizing the impact of population movements on traffic dominance. This comprehensive analysis provides crucial insights into the strategic management and development of urban traffic infrastructure in Shanghai.

Subway track foundation settlement deformation prediction based on the BiLSTM-AdaBoost model

The rapid economic expansion has spurred extensive construction near subway networks, impacting the stability of their track foundations. Consequently, it’s crucial to monitor and predict settlement in subway track foundations. However, the dynamic deformation patterns often exhibit nonlinearity and non-stationarity, posing challenges for traditional linear regression models. To tackle this, our study integrates the BiLSTM (bi-directional long short-term memory) network with the AdaBoost ensemble learning algorithm. Using settlement data from Shanghai metro monitoring points, the model is trained and evaluated employing R2 (coefficient of determination), MAE (mean absolute error), and RMSE (root mean square error). Results show that our proposed model displays superior predictive accuracy compared to the LSTM and the BiLSTM, with an average training set R2 of 0.99, test set R2 of 0.78, average MAE of 0.32 mm, and average RMSE of 0.4 mm. Consequently, for forecasting subway track foundation deformations, employing our network model ensures highly accurate predictive capabilities.

Mining Multimodal Travel Mobilities with Big Ridership Data: Comparative Analysis of Subways and Taxis

Understanding traveler mobility in cities is significant for urban planning and traffic management. However, most traditional studies have focused on travel mobility in a single traffic mode. Only limited studies have focused on the travel mobility associated with multimodal transportation. Subways are considered a green travel mode with large capacity, while taxis are an energy-consuming travel mode that provides a personalized service. Exploring the relationship between subway mobility and taxi mobility is conducive to building a sustainable multimodal transportation system, such as one with mobility as a service (MaaS). In this study, we propose a framework for comparatively analyzing the travel mobilities associated with subways and taxis. Firstly, we divided taxi trips into three groups: competitive, cooperative, and complementary. Voronoi diagrams based on subway stations were introduced to divide regions. An entropy index was adopted to measure the mix of taxi trips. Secondly, subway and taxi trip networks were constructed based on the divided regions. The framework was tested based on the automatic fare collection (AFC) data and global positioning system (GPS) data of a subway in Beijing, China. The results showed that the proportions of taxi competition, taxi cooperation, and taxi complements were 9.1%, 35.6%, and 55.3%, respectively. The entropy was large in the central city and small in the suburbs. Moreover, it was found that the subway trip network was connected more closely than the taxi network. However, the unbalanced condition of taxis is more serious than that of the subway.

Overview and Analysis of Ground Subsidence along China’s Urban Subway Network Based on Synthetic Aperture Radar Interferometry

Deformation along a subway rail network is related to the safe operation of the subway and the stability of construction facilities on the surface, making long-term deformation monitoring imperative. Long-term monitoring of surface deformation along the subway network and statistical analysis of the overall deformation situation are lacking in China. Therefore, targeting 35 Chinese cities whose subway mileage exceeds 50 km, we extracted their surface deformation along subway networks between 2018 and 2022, using spaceborne synthetic aperture radar (SAR) interferometry (InSAR) technology and Sentinel-1 satellite data. We verified the results with the continuous global navigation satellite system (GNSS) stations’ data and found that the root mean square error (RMSE) of the InSAR results was 3.75 mm/year. Statistical analysis showed that ground subsidence along the subways was more prominent in Beijing, Tianjin, and other areas in the North China Plain, namely Kunming (which is dominated by karst landforms), as well as Shanghai, Guangzhou, Qingdao, and other coastal cities. In addition, an analysis revealed that the severity of surface subsidence correlated positively with a city’s gross domestic product (GDP) with a Pearson correlation of 0.787, since the higher the GDP, the more frequent the construction and maintenance of subway, and the more commuters there are, which in turn exacerbates the disturbance to the surface. Additionally, the type of land cover also affects the ground deformation. Our findings provide a reference for constructing, operating, and maintaining the urban subway systems in China.

Unraveling mechanisms and failures of stray current corrosion in urban Subway adjacent pipeline networks through statistical insights and 3D simulation

The escalating expansion of urban subway systems in recent years has accentuated the issue of stray current corrosion within pipeline networks, emerging as a critical concern for urban safety. This paper delves into the intricate interplay between these phenomena, employing data-driven statistical analyses to elucidate the coupling characteristics between subway lines and the occurrence of failures in adjacent buried pipelines. An advanced three-dimensional finite element model was developed for stray current corrosion in pipelines, seamlessly integrating empirical data and physics-based modeling, which is to uncover the spatial nuances and multifaceted impacts on subway pipeline corrosion from both macro and micro perspectives under varying influencing factors. The study unveils a pronounced geographical and functional affinity between urban subway networks and metallic pipeline networks. The coupling attributes between subway systems and pipelines, such as distance, angle, and pipeline-specific characteristics including material and age, assume pivotal roles. The results further emphasize the hierarchical order of influence, with stray current intensity holding the greatest sway, followed by the distance between subway and pipelines, the angle between them, and soil resistivity. This paper offers a comprehensive investigation of the interrelationships and influential factors between subway systems and adjacent pipelines. It contributes to the mitigation and management of stray current corrosion in pipelines induced by nearby rail transit, thereby enhancing the resilience of both subway and pipeline networks within urban areas.

Model validation and Robust State Feedback control for nonlinear subway traffic networks

This paper develops a nonlinear Discrete-Event (DE) model for Intersecting Lines (ILs) of a Subway Network (SN). A class of Monte Carlo (MC) methods based on repeated random values is employed to validate the model. Model validation is performed by generating random values for delay rates and external disturbances. After model validation, a Robust State Feedback (RSF) controller is designed to compensate the delays caused by disturbances. Simulations are performed based on the characteristics and actual data of lines 2 and 4 of the Tehran subway to demonstrate the effectiveness of the proposed approach. Simulation results show the accuracy and proper performance of the proposed model. In addition, the closed-loop responses demonstrate the effectiveness of the proposed RSF controller for centralized and decentralized control configurations.

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.).

Entropy-Based Node Importance Identification Method for Public Transportation Infrastructure Coupled Networks: A Case Study of Chengdu.

Public transportation infrastructure is a typical, complex, coupled network that is usually composed of connected bus lines and subway networks. This study proposes an entropy-based node importance identification method for this type of coupled network that is helpful for the integrated planning of urban public transport and traffic flows, as well as enhancing network information dissemination and maintaining network resilience. The proposed method develops a systematic entropy-based metric based on five centrality metrics, namely the degree centrality (DC), betweenness centrality (BC), closeness centrality (CC), eigenvector centrality (EC), and clustering coefficient (CCO). It then identifies the most important nodes in the coupled networks by considering the information entropy of the nodes and their neighboring ones. To evaluate the performance of the proposed method, a bus-subway coupled network in Chengdu, containing 10,652 nodes and 15,476 edges, is employed as a case study. Four network resilience assessment metrics, namely the maximum connectivity coefficient (MCC), network efficiency (NE), susceptibility (S), and natural connectivity (NC), were used to conduct group experiments. The experimental results demonstrate the following: (1) the multi-functional fitting analysis improves the analytical accuracy by 30% as compared to fitting with power law functions only; (2) for both CC and CCO, the improved metric's performance in important node identification is greatly improved, and it demonstrates good network resilience.

A Coupling Model for Measuring the Substitution of Subways for Buses during Snowstorms: A Case Study of Shenyang, China

The development of integrated public transportation networks has received widespread attention in recent years. Especially in global northern cities, improving the substitution of subways for buses could meet population travel demand during snowstorms, which minimizes the impact of snowstorms on the public transportation network. Furthermore, the development of rail transit is conducive to the intensive and efficient use of land resources. Therefore, in this study, we selected a northern Chinese city, Shenyang, as a case study. For obtaining the population travel demand, we collected the actual population flow data in the morning and evening peaks during snowstorms. The network analysis was used to identify the loopholes and key stations in the subway and bus networks, respectively. A coupling model was built to measure the coupling value of each station in the subway and bus networks, according to its population travel demand and supply capacity, which was further used to measure the substitution of subways for buses in the morning and evening peaks during snowstorms. The results indicate that some subway stations were in a coupling state, while their surrounding bus stations were in a decoupling state. These subway stations could replace the bus stations to reduce the impact and damage of snowstorms on public transportation network. However, some subway stations and the surrounding bus stations were all in a decoupling state, which were under great pressure to meet the population commuting demand during snowstorms. This study can provide insight into optimizing public transportation network planning and design in many northern regions and help to coordinate land and transportation utilization.

Subway network optimization and passenger travel experience

This conference paper examines the significance of subway network optimization in relation to passenger travel experience. It begins with an introduction that highlights the importance of subway networks in urban transportation and establishes the objectives of network optimization. A comprehensive literature review explores previous research on subway network optimization and passenger travel experience, identifying strengths, limitations, and research gaps. The paper then explores various methods of subway network optimization, including network structure optimization, train scheduling optimization, and station layout optimization, providing explanations of their principles and application scenarios. The impact of these methods on passenger travel experience is discussed, considering evaluation metrics such as crowding level, punctuality, transfer efficiency, and comfort. A case study of the China Metro system is then presented to illustrate the implementation process of network optimization and an analysis of its impact on passenger travel experience using evaluation metrics. The findings emphasize the importance of subway network optimization and its potential to enhance passenger satisfaction, connectivity, and sustainable urban development. The conclusion connects with the paper thesis and main findings that underscore the significance of subway network optimization, proposing future research directions and exploring the relationship between optimization efforts and passenger travel experience. Therefore, the paper will help understand why subway network optimization and gives insights for enhancing passenger travel experience in urban subway systems.

Long-term exposure to PM10 and respiratory health among Parisian subway workers

Exposure to ambient PM10 may increase the risk of chronic obstructive pulmonary disease (COPD) and lung function decline. We evaluated the long-term exposure to PM10 and its relationship with COPD prevalence and lung function in Parisian subway workers.Participants were randomly selected from a 15,000-subway worker cohort. Individual annual external exposure to PM10 (ePM10) was estimated using a company-specific job-exposure-matrix based on PM10 measurements conducted between 2004 and 2019 in the Parisian subway network. Mean annual inhaled PM10 exposure (iPM10) was modeled as function of ePM10 exposure, inhalation rate, and filtration efficiency of the respiratory protection used. COPD diagnosis was performed in March–May 2021 based on post-bronchodilator spirometry. The relationship between iPM10 and outcomes was assessed using logistic and linear regression models, adjusted for exposure duration and potential confounders.Amongst 254 participants with complete data, 17 were diagnosed as COPD. The mean employment duration was 23.2 ± 7.3years, with annual mean ePM10 of 71.8 ± 33.7 μg/m3 and iPM10 of 0.59 ± 0.27 μg/shift, respectively. A positive but statistically non-significant association was found for COPD prevalence with iPM10 (OR = 1.034, 95%-CI = 0.781; 1.369, per 100 ng/shift) and ePM10 (OR = 1.029, 95%-CI = 0.879; 1.207, per 10 μg/m3). No decline in lung function was associated with PM10 exposure. However, forced expiratory volume during the first second and forced vital capacity lower than normal were positively associated with exposure duration (OR = 1.125, 95%-CI = 1.004; 1.260 and OR = 1.171, 95%-CI = 0.989; 1.386 per year, respectively). Current smoking was strongly associated with COPD prevalence (OR = 6.85, 95%-CI = 1.87; 25.10) and most lung function parameters.This is the first study assessing the relationship between long-term exposure to subway PM10 and respiratory health in subway workers. The risk estimates related with subway PM10 exposure are compatible with those related to outdoor PM10 exposure in the large recent studies. Large cohorts of subway workers are necessary to confirm these findings.

Subway Automatic Ticket Booking System using Verilog

Using Verilog HDL language to research a subway automatic ticket booking system. The design of this subway ticketing system takes convenience, quickness and simplicity as the core, and takes saving time for passengers. It completes the main process of buying subway tickets for passengers. Firstly, this project studies the development of subway ticketing system at home and abroad, and then studies the basic components of subway ticketing system. The paper also simulates the ticket selection module, change processing module and display interface module on Xilinx Vivado. In the Verilog design, finite state machines (FSMs) are employed to model the various states of the ticket booking process. These FSMs ensure smooth transitions between states, enabling the system to handle scenarios such as ticket availability checks, payment processing, and generating tickets. Additionally, the communication interface is responsible for securely transmitting booking information to the subway network, ensuring seamless integration with the overall subway infrastructure.

Multi-attribute fire safety evaluation of subway stations based on FANP – FGRA – Cloud model

With the rapid development of subway network, the fire safety of subway stations is becoming outstandingly important in the operation process. But there seem to be few studies developing quantitative models in the existing approaches. To this end, this study aims to develop a multi-attribute hybrid method by combining Fuzzy Analytic Network Process (FANP), and Fuzzy Grey Relational Analysis (FGRA) with Cloud model to evaluate fire safety of them. This research comprehensively sorts out and identifies high-frequency hazard factors associated with fire accidents at stations from personal, equipment, environmental and management aspects, and simultaneously explores dependencies and interactions among them. Seven typical transfer stations of Changsha subway network in China are studied by using the proposed approach, and the results show that: (1) the fire safety order from the best to the worst determined is FBH (A2), SWGY (A7), CSRS (A6), YWZ (A1), HTL (A5), HJT (A4) and WYGC (A3); (2) through comparing the results obtained by the proposed method with that determined fuzzy TOPSIS and fuzzy sum weighted method, it is proved that the method developed in this study is reliable and effective; and (3) sensitivity analysis indicates that the ranking order of stations is influenced by the fluctuation of criterion and sub-criterion weights.

3-Strategy evolutionary game model for operation extensions of subway networks

With the growing demand for night travel and the development of the night-time economy, extending the operation time of metropolitan subway networks has become essential. This paper proposes a 2-population 3-strategy evolutionary game model for subway network operation extension decisions, which considers the interests of subway companies, passengers, and the government while addressing transfer failure between lines due to uncoordinated subway operation time adjustments. The study resolves the Evolutionary Equilibria (EEs) of the replicator dynamics system and identifies key factors influencing the evolutionary path using theoretical calculations and a ternary phase diagram. Additionally, an extended game model investigates the impact of adjustment minutes. To verify the models, a case study based on the Beijing subway network is conducted. We show that the rule of maximum total fitness ensures the priority of EE strategies for all lines, selecting Nash equilibrium strategies for the lines which cannot select EE in a vastly reduced network strategy profile space (from 729 to 2). The study concludes that combining subsidy measures can eliminate multiple equilibria produced by implementing only penalty measures, and combining penalty measures can have the effect of making small subsidies as effective as large subsidies. The variation of the satisfaction/loyalty affects the system equilibrium only when both game players value passenger satisfaction/loyalty. Finally, we find that the proper adjustment minutes not only can make operation extension a long-term best strategy but also increase the payoff (106,158 RMB/year) and enhance the occupancy rate (20 %) of the last train. The study provides a theoretical foundation for the government to regulate the operation extension of subway companies from multiple perspectives.