Subway System Research Articles

Research on entropy weight multiple criteria decision‐making evaluation of metro network vulnerability

AbstractWhen the subway network system has insufficient resilience and strong vulnerability, the network operation function will be greatly attenuated or even lost due to the failure of key nodes or lines. Evaluating the vulnerability of the metro network is one of the keys to prevent and control risks. This paper proposes an entropy weight multiple criteria decision‐making evaluation method that combines the vulnerability indexes of agglomeration, network efficiency, and network traffic with the multiple criteria decision‐making method, classifies the vulnerability of subway stations with a clustering idea, and analyzes the most vulnerable area of the Beijing Metro network with the Newman fast algorithm. The results show that this method can evaluate the node vulnerability of metro networks more accurately and effectively by considering the topological vulnerability, functional vulnerability, and human flow function vulnerability of the networks. According to the node vulnerability value, the stations can be divided into five grades: The station at the intersection of the loop and radial lines and its adjacent stations have a higher vulnerability level. The vulnerability level of the downtown core area and suburban line terminal stations is relatively low, and the areas with the greatest vulnerability of the Beijing Metro network are mainly located on the east and west sides of the core city.

Characteristics of subway air quality and favorable locations for passengers waiting and taking the subway

With the increase of commuters in Beijing subway, the air quality inside subway systems has caused widespread apprehension. The particulate matter (PM) level and potential health risk of PM on the platforms and carriages of Open (O) system, Half-height platform door (HPD) system, Full-height platform door (FPD) system, Platform screen door (PSD) system were studied. The average PM2.5 concentration was approximately twice the Chinese National Ambient Air Quality Standard Class II threshold and ten times the WHO Global Air Quality Guidelines. The average PM2.5 and PM10 ratios on the platform and inside carriages were 0.81 and 0.85, respectively. Compared to the FPD and HPD, the PSD has the highest ability to prevent tunnel dust entering the platform, whereas, the FPD's preventive ability is insignificant. The maximum comprehensive indoor-air quality index (CIAI) values for platform PM2.5 and PM10 are at a very unhealthy risk level and unhealthy risk level, respectively. The average PM2.5 doses deposited by adult males in extrathoracic (ET), tracheobronchial (TB) and alveolar-interstitial (AI) during a day's round-trip ride were 163.7 μg, 9.6 μg and 24.1 μg, respectively. The PM concentration at the station platform nearer to the entrance location is greater than the middle of the platform and the center of the station hall. The PM concentration at the middle of the carriage between two doors is lower than that at the door. The research results have important guidance for constructing a subway environmental control system, station passenger flow command desk setting, passenger waiting and taking the subway to reduce the health risk.

Fire Risk Assessment of Subway Stations Based on Combination Weighting of Game Theory and TOPSIS Method

With the rapid development of urban modernization, traffic congestion, travel delays, and other related inconveniences have become central features in people’s daily lives. The development of subway transit systems has alleviated some of these problems. However, numerous underground subway stations lack adequate fire safety protections, and this can cause rescue difficulties in the event of fire. Once the fire occurs, there will be huge property losses and casualties. In addition, this can have a vicious impact on sustainable development. Therefore, in order to make prevention in advance and implement targeted measures, we should quantify the risk and calculate the fire risk value. In this study, through consulting experts and analysis of data obtained from Changzhou Railway Company and the Emergency Management Bureau, the fire risk index system of subway stations was determined. We calculated the index weight by selecting the combination weighting method of game theory to eliminate the limitations and dependence of subjective and objective evaluation methods. The idea of relative closeness degree in TOPSIS method iwas introduced to calculate the risk value of each subway station. Finally, the subway station risk value model was established, and the risk values for each subway station were calculated and sorted. According to expert advice and the literature review, we divided the risk level into five levels, very high; high; moderate; low and very low. The results shown that 2 subway stations on Line 1 have very high fire risk, 2 subway stations on Line 1 have high fire risk, 2 subway stations on Line 1 have moderate fire risk, 8 subway stations on Line 1 have low fire risk, and 13 subway stations on Line 1 have very low fire risk. We hope that through this evaluation model method and the results to bring some references for local rail companies. Meanwhile, this evaluation model method also promotes resilience and sustainability in social development.

Impacts of subway development on air pollution and vegetation in Tabriz and Shiraz, Iran

Introduction: Presently, air pollution is viewed as a critical environmental challenge that has deleterious effects on human health and ecosystems. The subway system is extensively developed in numerous countries with the objective of minimizing traffic congestion and pollutant emissions. The aim of the present study is to explore the impact of metro activities on air pollution and, subsequently, urban vegetation inside the two metropolises of Tabriz and Shiraz in comparison to prior years.
 Materials and methods: To assess air quality before and after the establishment of the metro, we collected average data for Particulate Matters less than 2.5 µm (PM2.5), Particulate Matters less than 10 µm (PM10), SO2, NO2, O3, and CO, as well as the Air Quality Index (AQI), retrieved from monitoring stations in Tabriz and Shiraz between 2014 and 2019. We used the average of the Normalized Difference Vegetation Index (NDVI) calculated by the Landsat 8 satellite in the second phase in order to numerically determine the status of urban vegetation across two timeframes.
 Results: Preliminary evidence revealed that the average concentration of pollutants in Tabriz, excluding NO2, fell after the launch of the metro system in 2016. Simultaneously, several pollutants, including O3, NO2, and PM2.5, and consequently the AQI, increased following the subway's establishment in Shiraz in 2017. Moreover, it was observed that decreasing emissions in Tabriz amplified vegetation, whereas reducing air quality in Shiraz lowered the NDVI values.
 Conclusion: Although it appears that the metro's operation improved environmental conditions in Tabriz, a similar outcome was not evident in Shiraz. Therefore, it is suggested that future studies consider meteorological variables whenever addressing the metro's efficiency.

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.

Modelling the walking behavior of pedestrians in the junction with chamfer zone of subway station

More and more passengers prefer to travel by subway system, and congestion is inevitable in the routine operation of subway stations. To alleviate congestion, junctions with chamfer zone (JCZs) are built in subway stations. The main intention of this work is to investigate the improvement of walking efficiency by the chamfer zone. Firstly, the specific walking behaviors of pedestrians in JCZs are investigated, and the principle of detour behavior is proposed. Secondly, a walking model considering the detour behavior is developed for pedestrians traversing the JCZ, and model parameters are calibrated by the Response Surface Methodology (RSM). Then, the influences of chamfer geometry on walking speed, walking time and time headways are simulated and analyzed. Results indicate that the walking efficiency will be improved by about 7.1% when the chamfer length is 1 meter. The interference among pedestrians is lower, and most time headways fall within the range of 0.1 ∼ 0.3 s, which means pedestrians walk steadily in the JCZ. Finally, the walking parameters of pedestrians in JCZs and corners are compared in various scenarios, and the matching correlation between chamfer length and turning angle is obtained.

Machine identification method of subway service quality based on smart card data

Abstract A new automatic evaluation method of subway service quality based on metro smart card data is proposed suitable for three different levels: station pair, railway line and subway network, which has merits of overcoming the previous lagging and subjective evaluation in the system of ‘questionnaire survey plus evaluation method’. First, passengers' travel time distribution for different operating periods in station OD pairs are introduced initially for service evaluation purposes and are classified into different groups in order to infer the station's operating characteristics at the different periods. Second, the classification is verified by K-means cluster analysis and K-S tests. Third, the service quality weight indicator is proposed to identify the service quality of the entire metro network from the dual perspectives of passengers and companies. Finally, the feasibility and rationality of the proposed method are verified by Shenzhen metro smart card data as an example. The new automated evaluation method of subway service quality is suitable for online and offline application. Highlights • A new machine identification method based on travel time distribution is proposed for estimating the service quality. • The proposed model employs the K-means clustering method to distinguish passenger's travel time distribution into different modes. • Characteristics of the different modes of passenger travel time distribution reflect different service levels of the urban subway system. • The method provides automatically identification of service levels for urban subway operations.

Prediction of Epidemic Transmission Path and Risk Management Method in Urban Subway

With the development of COVID-19, the epidemic prevention requirements of city subway system have become stricter. This study studies the transmission path of epidemic disease in city subway system. Using FLUENT software and AnyLogic software, the simulation models of subway platform ventilation structure and crowd behavior mode in subway system are constructed, respectively, and SEIR (vulnerable exposed affected recovered) is used as the general infection model of epidemic disease. According to the actual situation, the parameters such as shoulder width, flow, and moving speed of crowd are determined, and the simulation analysis of epidemic disease transmission in subway system is carried out. The analysis results show that the transmission speed of the disease in the subway will increase with the enhancement of the transmission capacity of the disease and the increase of the contact rate. When the disease transmission capacity is 0.14, the number of latent persons reaches the peak at 14.115 time units, which is 1374, and the number of patients reaches the peak at 28.541 time units, which is 1925. According to the simulation results, the simulation analysis results show that with the enhancement of disease transmission ability and the increase of exposure rate, the maximum number of latent and sick people in the subway environment will increase. The corresponding suggestions on risk management and control of infectious disease transmission in subway are put forward. The research results can provide a useful reference for the epidemic prevention management of urban subway transportation system in China.

Identification of Abnormal Vibration Signal of Subway Track Bed Based on Ultra-Weak FBG Sensing Array Combined with Unsupervised Learning Network

The performance of the passing train and the structural state of the track bed are the concerns regarding the safe operation of subways. Monitoring the vibration response of the track bed structure and identifying abnormal signals within it will help address both of these concerns. Given that it is difficult to collect abnormal samples that are symmetric to those of the normal state of the structure in actual engineering, this paper proposes an unsupervised learning-based methodology for identifying the abnormal signals of the track beds detected by the ultra-weak fiber optic Bragg grating sensing array. For an actual subway tunnel monitoring system, an unsupervised learning network was trained by using a sufficient amount of vibration signals of the track bed collected when trains passed under normal conditions, which was used to quantify the deviations caused by anomalies. An experiment to validate the proposed procedures was designed and implemented according to the obtained normal and abnormal samples. The abnormal vibration samples of the track beds in the experiment came from two parts and were defined as three levels. One part of it stemmed from the vibration responses under the worn wheels of a train detected during system operation. The remaining abnormal samples were simulated by superimposing perturbations in the normal samples. The experimental results demonstrated that the established unsupervised learning network and the selected metric for quantifying error sequences can serve the threshold selection well based on the receiver operating characteristic curve. Moreover, the discussion results of the comparative tests also illustrated that the average results of accuracy and F1-score of the proposed network were at least 11% and 13% higher than those of the comparison networks, respectively.

Close contact behavior-based COVID-19 transmission and interventions in a subway system

During COVID-19 pandemic, analysis on virus exposure and intervention efficiency in public transports based on real passenger’s close contact behaviors is critical to curb infectious disease transmission. A monitoring device was developed to gather a total of 145,821 close contact data in subways based on semi-supervision learning. A virus transmission model considering both short- and long-range inhalation and deposition was established to calculate the virus exposure. During rush-hour, short-range inhalation exposure is 3.2 times higher than deposition exposure and 7.5 times higher than long-range inhalation exposure of all passengers in the subway. The close contact rate was 56.1 % and the average interpersonal distance was 0.8 m. Face-to-back was the main pattern during close contact. Comparing with random distribution, if all passengers stand facing in the same direction, personal virus exposure through inhalation (deposition) can be reduced by 74.1 % (98.5 %). If the talk rate was decreased from 20 % to 5 %, the inhalation (deposition) exposure can be reduced by 69.3 % (73.8 %). In addition, we found that virus exposure could be reduced by 82.0 % if all passengers wear surgical masks. This study provides scientific support for COVID-19 prevention and control in subways based on real human close contact behaviors.

Optimal Design of Subway Train Cross-Line Operation Scheme Based on Passenger Smart Card Data

The network operation of the subway can reduce the number of passenger transfers and improve subway operation efficiency. Based on the subway smart card data, this paper proposes an optimal design method for the cross-line operation scheme of subway trains. The method firstly calculates the OD matrix between subway stations, the passenger section flow, and the transfer flow according to the passenger smart card data. It then optimizes the design of the subway train cross-line operation plan, including determining the routing type of cross-line operation and the number of trains running. Finally, for the lines with cross-line operation conditions in the urban subway system, we design the cross-line operation schemes under all possible combinations. According to the volume of the cross-line passenger flow that the cross-line trains can carry, the top-ranked operation plans are preferably recommended. Taking the Chengdu subway network as an example, the research results show that, in the Chengdu subway network, the North Railway Station, Xibo City Station, and South Railway Station bear the highest transfer demand. The transfer demands that can be undertaken by cross-line trains are 23,934, 16,710, and 13,024 trips per hour, respectively. This shows that the proposed design method can accurately and reasonably screen out the transfer stations and lines with an urgent need for cross-line trains.

From garbage to COVID-19: theorizing ‘Multilingual Commanding Urgency’ in the linguistic landscape

AbstractAcross the globe signage which conveys directives regarding appropriate behavior in public, such as ‘Do Not Enter’ signs, is made multilingual in ways that other signage is not. This paper examines two examples of multilingualism in directive signs within Seoul, South Korea in order to theorize what gives rise to multilingualism in directive signage while other signage remains monolingual. Examination of Vietnamese and Arabic on signs prohibiting the illegal disposal of household garbage on side streets in Seoul, and English, Chinese, and Japanese on mask-required due to COVID-19 signs within the Seoul subway system allows for a robust analysis of what shapes the inclusion of additional languages on directive signage. We posit the construction of a differently speaking other who is seen as likely to disobey stated regulations alongside the desire by authorities to minimize the effort required to respond to rule breaking results in amultilingual commanding urgencythat shapes multilingualism in directive signage. The concept of multilingual commanding urgency emphasizes the role enforcement practices have in shaping multilingualism, an important development in understanding this form of signage. Multilingual commanding urgency is especially relevant as it shapes signage deployed in emergency contexts such as the COVID-19 pandemic.

Atmospheric iron particles in PM2.5 from a subway station, Beijing, China

Particulate matter pollution in the subway station's atmosphere can seriously influence the air quality and impacts on the health of subway workers and commuters. In this study, PM2.5 samples were collected from different locations within a subway station in Beijing, and the individual particles were analyzed for morphology and composition by Transmission Electron Microscopy with Energy Dispersive X-ray Spectrometry (TEM-EDX). The results showed that the concentration of PM2.5 in subway stations was affected by both indoor and outdoor sources. Particles generated by train-related sources such as resuspension, wheel, rail, brake and collector shoe abrasion were a significant source of airborne pollution in the subway atmosphere. Within the subway station PM2.5, Fe was the dominant element, and was detected in more than 75% of all particles analyzed. The Fe-rich particles were identified in railway carriages (79.4%), station concourse (65.3%), and platforms (61.3%). The geometric mean diameter of Fe-rich particles was 0.34 μm, which was smaller than that of all detected particles. Cr, Mn and other metals were often detected in the Fe-rich particles, reflecting metal alloys used in the wheels and tracks. A better understanding of the particle distribution around different areas of the subway system and the physicochemical characteristics of these Fe-rich particles is critical in developing a meaningful assessment of the risk posed by particles in the subway atmosphere.

Data fusion of multiple machine intelligent systems for the condition assessment of subway structures

Water intrusion through soil is considered the most significant structural issue and the major cause of concrete degradation in subway networks. An enormous amount of water infiltration may expedite the deterioration mechanisms, such as moisture marks, spalling, scaling, and cracks. These mechanisms can compromise the structural durability and jeopardize people’s safety. The condition assessment of concrete infrastructure is predominantly conducted based of visual inspection techniques, which are costly, time-consuming, and error prone. In this research, two main models for the condition assessment of subway networks are proposed. First, image processing techniques and machine intelligent systems are streamlined through successive operations to detect and quantify multiple surface defects automatically. Spatial and frequency domain filters are used to enhance the image clues, in tandem with artificial neural networks (ANNs) and regression analysis (RA) for defect recognition. The Monte Carlo simulation (MCS) is then leveraged to deliver advanced optimization and accurate estimation for each defect’s condition index in the subway element. The developed method was implemented on four stations in Montréal subway systems, whereby the performance of ANNs and RA was validated through R2 as 0.928 and 0.957, respectively. Moreover, the MCS forecast precision was recorded as 95% percentile, which proves the efficacy of the developed models. This research provides insights for infrastructure managers about maintenance and intervention plans in order to prioritize their spending policies.

Mathematical Modeling and Optimal Control of Underground Ventilation and Air Conditioning in Station

The underground ventilation and air-conditioning system of the station accounts for about 40% of the total energy consumption of the subway. One of the important reasons is the cooling load of the station. The related control process has typical nonlinear, time-varying, and large inertia characteristics, and the traditional PID control algorithm is difficult to achieve the ideal control effect. Therefore, it is of great significance to study the application of the intelligent algorithm in subway ventilation and air-conditioning control systems to improve the control effect. Based on the analysis of the coupling characteristics of temperature and humidity control in subway air-conditioning systems, this paper designs the feedforward decoupling link according to the identification model and analyzes the control curves with and without the decoupling link when using conventional PID control, which shows the effectiveness of the method proposed in this paper.

Meming the Catch-22: Doing security where the steel wheel hits the rail

While Internet memes are often treated as artifacts which play with broad cultural elements, they can also take on distinctive meanings within narrow communities of practice or organizational membership. This article demonstrates how interrogating a certain interpretation of a meme, and a sort of humor found within it, can reveal elements of viewers’ situated experience. In other words, I argue that when we get a joke, we begin to understand a world. I examine the particular reception of a meme — a parody of a familiar New York City subway security poster — by workers in that subway system, and show how a dark gallows humor they found in the meme draws from problems in their workplace. In turn, the world betrayed by that gallows humor illustrates challenges to organizational efforts and public policy to confront emergencies and crises. Unpacking the humor and even ridicule seen in memes in specific organizational and work settings, this article suggests, can offer shortcuts to reveal overlooked — but vital — perspectives in critical contexts.

Commuters health risk associated with particulate matter exposures in subway system – Globally

The effects of air pollution within subway systems on public health has far been overlooked. A few previous studies have warned that particulate matter (PM)-bound metals in subway air pose a serious threat to public health, but these have been limited by insufficient risk measurement and limited regional data. Present study aimed to determine the levels of human health risk associated with PM10 and PM2.5-bound trace metals in twelve city subways across North America, Asia, and Europe using the human exposure method. Human health risks were estimated via ingestion, inhalation, and dermal exposure methods. The Monte Carlo statistical method using 50,000 iterations was adopted to calculate the probable risk levels for children and adults. Significant levels of non-cancer and cancer risks (CR) were found in subway systems at all regions, regardless of age, although CR were relatively higher in adults. Inhalation and dermal absorption, in which the acceptable range of carcinogenic risk for subway systems was exceeded. Exposure through ingestion made the greatest contributions to overall CR. Among the all subways Tehran and Seoul were estimated as higher CR zone (Total CR > 10−3). Therefore, the Asian subway systems is not only polluted with respect to particulate matter but also recognized as a highly CR zone among the other continents. Cr and Pb have been identified as PM-bound carcinogenic elements that significantly influence cancer risk by inhalation/dermal absorption and ingestion, respectively. Although, the subway is a convenient transportation system for millions of city inhabitants globally, but these findings support the view that indoor air pollutant levels in subway systems can have a serious impact on the cancerous and non-cancerous risks to human health. Strengthening the regulation of indoor air quality or the application of active air purification techniques in subway systems are highly suggested to manage the worst situation.

Spatial distributions of restaurants emerging from pedestrian behavior and online information sharing

We analyze the spatial distributions of restaurants in Seoul to examine the effects of the proximity to a transportation system as well as online information sharing on urban living facilities. Despite its complexity, restaurants are spread in a self-similar manner, forming clusters with the fractal dimension approximately given by 1.62. The probability density of distances between each restaurant and the nearest station shows that the locations of restaurants are associated extensively with the Seoul metropolitan subway system. Moreover, we explore the effects of online information sharing by choosing a subset of restaurants having a large number of online reviews. The distribution of these most-reviewed restaurants exhibits similar but somewhat different geographical patterns. To explain observed features based on data analysis, we propose a mathematical model taking into account the pedestrians and exploration paths as well as land prices around subway stations. Parametrization in terms of the characteristic distance and scaling exponents makes it possible to understand, with a single formula, the seemingly different spatial patterns between all restaurants and most-reviewed ones, manifesting universality in such phenomena.

Passengers’ Sensitivity and Adaptive Behaviors to Health Risks in the Subway Microenvironment: A Case Study in Nanjing, China

Passenger behavior in subways has recently become a matter of great concern, with more attention being paid to the health risks of the subway microenvironment (sub-ME). This paper aimed to provide guidance for subway passengers on better adapting to the health risks presented by the sub-ME. A questionnaire-based survey was conducted in Nanjing, China, and descriptive analysis and a one-way analysis of variance were performed to understand the sensitivity levels of subway passengers and analyze their adaptive behaviors, based on their sensitivity to sub-ME health risks. The results showed that passengers over 66 years old and those who are frequently sick are more sensitive to the presented health risks. Additionally, passengers traveling for longer and those traveling in rush hours are more sensitive to sub-ME health risks. We also found that individual characteristics, knowledge structure, and information communication all influence passengers’ adaptive behaviors. It was ascertained that those with a positive attitude and those who had previously suffered from environmentally influenced diseases, as well as those who studied an environment-related subject, tended to demonstrate more adaptive behaviors. Moreover, passengers who are very familiar with the subway information communication channels and the related information adapted better to the health risks of the sub-ME. Our findings are beneficial for improving passengers’ adaptability to the health risks presented by the sub-ME and for promoting the sustainable operation of subway systems.

Managing public transit during a pandemic: The trade-off between safety and mobility

During a pandemic such as COVID-19, managing public transit effectively becomes a critical policy decision. On the one hand, efficient transportation plays a pivotal role in enabling the movement of essential workers and keeping the economy moving. On the other hand, public transit can be a vector for disease propagation due to travelers’ proximity within shared and enclosed spaces. Without strategic preparedness, mass transit facilities are potential hotbeds for spreading infectious diseases. Thus, transportation agencies face a complex trade-off when developing context-specific operating strategies for public transit. This work provides a network-based analysis framework for understanding this trade-off, as well as tools for calculating targeted commute restrictions under different policy constraints, e.g., regarding public health considerations (limiting infection levels) and economic activity (limiting the reduction in travel). The resulting plans ensure that the traffic flow restrictions imposed on each route are adaptive to the time-varying epidemic dynamics. A case study based on the COVID-19 pandemic reveals that a well-planned subway system in New York City can sustain 88% of transit flow while reducing the risk of disease transmission by 50% relative to fully-loaded public transit systems. Transport policy-makers can exploit this optimization-based framework to address safety-and-mobility trade-offs and make proactive transit management plans during an epidemic outbreak.