Subway Station Research Articles

역사 특성이 역세권 상권 매출액에 미치는 영향에 관한 연구

The purpose of this study was to analyze the determinants of commercial sales of these industries located in the station influence area and to clearly identify the difference. The subjects of this study are beauty salons for service businesses, coffee/drinks for restaurants, and convenience stores for retail businesses, and these industries are characterized by the fact that the number of businesses in the top 100 living sectors selected by the National Tax Service is within the 15th place and are mainly concentrated in the station influence area. As a result of the analysis, it was found that hair salons, coffee/drink stores, and convenience stores were affected by sales differently depending on the characteristics of subway stations, regional characteristics, and location characteristics, respectively. This study aimed to serve as an objective and practical basis for decision-making by prospective entrepreneurs or related policy makers, targeting beauty salons, coffee/drink stores, and convenience stores that are close to people's lives and have a high influence in the station area. However, due to the limitations of available data, the characteristics of individual stores cannot be examined, and the study is limited to specific industries.

A model for simulating PM2.5 concentration in subway systems

Due to adverse effects on health, the PM2.5 of subway systems is increasingly attracting people's attention. Although scholars have conducted many experimental studies on the characteristics of PM2.5 in subway stations, theoretical research on the propagation law of PM2.5 in subway systems is still lacking. In this study, we established a mathematical model for the PM2.5 concentration in the subway system based on the principle of mass conservation, including a PM2.5 generation model for trains operating in tunnels and a network model for calculating the ventilation rate, to simulate the variation of PM2.5 concentration in various areas of the subway system. The model was used to simulate the PM2.5 concentration of a subway line in Nanjing, China and on-site testing was conducted to verify the simulation results, with an error was within 10%. The simulation results quantitatively illustrate the influence of train density and ambient concentrations on PM2.5 variation in the subway system and the comparison with studies in other countries has also verified the applicability. This model could be used based on actual subway lines to simulate the PM2.5 concentration at any time during subway operation, thereby providing effective guidance for improving the environmental air quality of subway stations and optimizing the design of environmental control systems.

The heterogeneous effects of dockless bike-sharing usage intensity on house prices near subway stations

Dockless bike-sharing (DBS) systems have emerged as a popular mode of transportation in urban areas. While existing literature has explored the potential effects of DBS on urban systems, there is limited research on its impact on housing markets. This study addresses this gap by investigating the heterogeneous effects of DBS usage intensity on house prices at various distances from subway stations in Shanghai. Utilizing Mobike trip data and a dataset of 50,837 second-hand houses sold between May 2016 and December 2018, the analysis reveals that DBS usage intensity positively impacts house prices in areas outside 800 m and within 3000 m from subway stations, resulting in a 1.4 % increase in house prices for every 1,000 DBS rides within a 500 m radius. The study also finds that the marginal effect of DBS usage intensity on house prices is contingent on the distance from subway stations. For distances shorter than 2.33 km, the marginal effect rises with increasing distance. Conversely, for distances exceeding 2.33 km, the marginal effect declines and turns insignificant beyond 3 km. These findings imply that the positive influence of DBS on house prices is more pronounced in areas that are neither too close nor too far from subway stations, where people are more likely to use DBS to connect to subway networks. The findings of this study contribute to a better understanding of the complex relationship between DBS and real estate markets.

Assessment of carbon emissions from TOD subway first/last mile trips based on level classification

Many cities around the world have developed Transit-oriented development (TOD) based on subway systems, expecting to alleviate problems such as carbon emissions and pollution in the transportation sector. Although previous studies have proved the contributions to mitigate congestion and carbon emission pressure, there is a lack of evaluation on whether TOD can promote green mode choice and effectively reduce carbon emissions of first/last-mile trips in existing studies. This study aims to verify that subway stations with high TOD levels have a positive effect on reducing the carbon emissions of first/last-mile trips. We evaluate subway stations in Hangzhou and classify them into two categories based on their TOD levels. Then the carbon emission of ten typical subway stations is calculated based on survey data of first/last-mile trips and empirical formula. The nested logit model (NL) is used to analyze the correlation between the choice of first/last-mile mode and personal and environmental factors. The case results show that subway stations with a higher TOD level usually exhibit higher daily passenger flows (increased by approximately two-fold) and lower per capita carbon emissions (reduced by approximately 70 %). These findings proved that the high TOD level has a positive impact on carbon emissions of subway first/last mile trips, which could provide data support and insights for TOD development, especially in developing countries.

Numerical analysis on the seismic performance of subway station reinforced by ECC-BFRP

In areas with high seismic activity, enhancing the seismic performance of subway station structures is of paramount importance. This paper presents a novel approach involving the application of an ECC-BFRP (Engineered Cementitious Composite with Basalt Fiber Reinforced Polymer) composite layer to reinforce central columns of subway stations situated in loess site. To evaluate the effectiveness of this reinforcement method, a three-dimensional finite element model of subway station structure, accounting for soil-structure interaction (SSI) effects, was meticulously developed using the finite element software Abaqus. The seismic responses of the subway stations with various reinforcement scenarios such as reinforced by the cement mortar, ECC, Mortar-BFRP and ECC-BFRP layer were respectively analyzed in comparison to the unreinforced station. Both the structural displacement response and the changing pattern of damage development for the overall and local structural members were obtained. The results indicated that the ECC-BFRP composite layer could effectively improve the stiffness of the central column and reduce the seismic response of the station. Meanwhile, the seismic damage of the central column was significantly reduced, and the seismic damage distribution of other structural members was uniformly distributed. Consequently, the seismic performance of the station was effectively improved. The research results provided valuable guidance for the seismic design of underground structures.

Short-Term Interval Prediction of Inbound Passenger Flow of Subway Station under Failure Events

Accurate forecasting of subway passenger flows is considered essential for the development of efficient train schedules. However, transport capacity constraints as well as station congestion can be caused by unexpected concerns with trains or power supply, which endanger passenger safety. Predicting passenger flows at the time of a fault is particularly challenging due to the low probability of failure and the complexity of the factors involved. In addition, deviation from the observed value may be resulted by the point-in-time prediction of passenger flow, thus affecting the efficiency of passenger flow control measures. To address this concern, a three-stage A-LSTM prediction model utilizing an attention mechanism and a double-layer LSTM (Long Short-Term Memory) neural network has been proposed. The model is used to map the impact of fault events on subway transport capacity with respect to delays onto the inbound passenger flow. By analyzing the data from the subway system in a metropolitan city of China, the range of passenger flow fluctuations in 10-minute intervals will be precisely predicted and applied to different subway stations.

Endurance time analysis for seismic performance of underground structures subjected to mainshock–aftershock sequences

The seismic response analysis of subway underground structures only considers the impact of a single mainshock, ignoring the aftershocks, which may cause more serious secondary damage within a short time after strong earthquakes. To investigate the seismic performance of underground structures under sequential earthquakes and improve the understanding of aftershocks, we analyzed the dynamic response of subway station structures under mainshock–aftershock sequences. A typical two-story and three-span subway station was selected as the prototype, and a finite element model of a soil–underground structure interaction was established. Based on the basic concept of endurance time and design response spectrum of relevant seismic standards in China, an endurance time acceleration function was generated, and nine mainshock–aftershock sequences were constructed. Thereafter, the seismic responses of subway stations under the action of mainshock–aftershock sequences, such as lateral deformation characteristics and damage failure law, were discussed and analyzed. The results show that the endurance time method can be used as a new and efficient method to study the seismic performance of underground structures subjected to mainshock–aftershock sequences. The effects of aftershocks on the damage and lateral displacement of underground structures were preliminarily revealed. The structural deformation response of each layer varies greatly due to the different damage states of the mainshock. The maximum story drift θ of the structure increases with the increase of loading seismic intensity. From the perspective of seismic damage and relative deformation, the structural dynamic response, considering the effect of sequential earthquakes, is greater than that of the mainshock alone, which reflects the disadvantage of aftershocks in seismic design. The results provide a reference for seismic analysis and damage assessment of structures under sequential earthquakes.

The composition of the bacterial communities collected from the PM10 samples inside the Seoul subway and railway station

Health implications of indoor air quality (IAQ) have drawn more attention since the COVID epidemic. There are many different kinds of studies done on how IAQ affects people’s well-being. There hasn’t been much research that looks at the microbiological composition of the aerosol in subway transit systems. In this work, for the first time, we examined the aerosol bacterial abundance, diversity, and composition in the microbiome of the Seoul subway and train stations using DNA isolated from the PM10 samples from each station (three subway and two KTX stations). The average PM10 mass concentration collected on the respective platform was 41.862 µg/m3, with the highest average value of 45.95 µg/m3 and the lowest of 39.25 µg/m3. The bacterial microbiomes mainly constituted bacterial species of soil and environmental origin (e.g., Acinetobacter, Brevundimonas, Lysinibacillus, Clostridiodes) with fewer from human sources (Flaviflexus, Staphylococcus). This study highlights the relationship between microbiome diversity and PM10 mass concentration contributed by outdoor air and commuters in South Korea’s subway and train stations. This study gives insights into the microbiome diversity, the source, and the susceptibility of public transports in disease spreading.

Emergency evacuation based on long range communication technology

The complexity and uncertainty of a fire in a large public building with complex structure leads to risks during emergency evacuation. Therefore, it is crucial to conduct intelligent emergency evacuation according to the situational development of the accident environment, the crowding degree of evacuation routes, as well as the reliability and flexibility of data transmission in large complex building structures. In this study, we develop an Intelligent Emergency Evacuation System (IEES) based on the Long Range (LoRa) communication technology and the Evacuation Decision Optimization Model (EDOM). The IEES comprises three parts, i.e., multi-sensor Fire Monitoring System (FMS), Central Control System (CCS), and Wireless Evacuation Indication System (WEIS). The LoRa communication technology is used for data transmission among the three parts of the IEES to assist large scale evacuation. The EDOM is developed to support the CCS based on the improved adaptive ant colony algorithm. To facilitate the evacuation decision optimization, the initial node database in the evacuation spatial network is established intelligently on the Building Information Modeling (BIM) platform. An on-site application in a subway station with three underground floors is conducted. The results demonstrate that the IEES can guide people in evacuating from hazardous locations according to the state of the fire. Moreover, the phenomena of crowd retention and congestion at a few certain evacuation exits are considerably reduced to achieve global optimization of the evacuation path and accomplish the objective of intelligent emergency evacuation. Furthermore, WEIS can maintain normal operation and have good visibility in the fire environment of heat radiation and thick smoke. The IEES can realize dual wireless indication of vision and sound perception, and its response time is slightly affected by the building structure and transmission distance. The electromagnetic signal of electrical equipment and the operational state of vehicles in and out of the subway station causes the response time to a relatively larger lag. The response time of the IEES is delayed up to 14 ms. Nonetheless, it fulfills the requirements of response time of evacuation indication signs stated in the Technical Standard for Fire Emergency Lighting and Evacuate Indicating System.

Study on the Influence of Deep Soil Liquefaction on the Seismic Response of Subway Stations

Subway systems are a crucial component of urban public transportation, especially in terms of safety during seismic events. Soil liquefaction triggered by earthquakes is one of the key factors that can lead to underground structural damage. This study investigates the impact of deep soil liquefaction on the response of subway station structures during seismic activity, aiming to provide evidence and suggestions for earthquake-resistant measures in underground constructions. The advanced finite element software PLAXIS was utilized for dynamic numerical simulations. Non-linear dynamic analysis methods were employed to construct models of subway stations and the surrounding soil layers, including soil–structure interactions. The UBC3D-PLM liquefaction constitutive model was applied to describe the liquefaction behavior of soil layers, while the HS constitutive model was used to depict the dynamic characteristics of non-liquefied soil layers. The study examined the influence of deep soil liquefaction on the dynamic response of subway station structures under different seismic waves. The findings indicate that deep soil liquefaction significantly increases the vertical displacement and acceleration responses of subway stations compared to non-liquefied conditions. The liquefaction behavior of deep soil layers leads to increased horizontal effective stress on both sides of the structure, thereby increasing the horizontal deformation of the structure and posing a potential threat to the safety and functionality of subway stations. This research employed detailed numerical simulation methods, incorporating the non-linear characteristics of deep soil layer liquefaction, providing an analytical framework based on regulatory standards for evaluating the impact of deep soil liquefaction on the seismic responses of subway stations. Compared to traditional studies, this paper significantly enhances simulation precision and practical applicability. Results from this research indicate that deep soil layer liquefaction poses a non-negligible risk to the structural safety of subway stations during earthquakes. Therefore, the issue of deep soil liquefaction should receive increased attention in engineering design and construction, with effective prevention and mitigation measures being implemented.

Study on Hydration Heat Release Model and Its Influence Coefficient of Addition Concrete

The concrete of the main structure of subway stations is highly constrained, so the temperature effect and shrinkage effect generated during the construction process often cause large restraint stress, thus causing the problem of structural cracking. Therefore, low-heat-of-hydration cements are usually used in such construction work to reduce the temperature during the aging of concrete. Therefore, based on the hydration heat release subprogram of concrete, an effective numerical simulation was carried out, and compared with the semi-adiabatic temperature rise test, a method of numerical calculation and parameter fitting to solve the influence coefficient of the hydration heat model was proposed, and the sensitivity of the hydration heat release model and the influence coefficient for several concrete mixtures with additives was studied. The main conclusions are as follows: (1) Combined with the measured temperature curves of several types of mineral-admixed concrete, the composite exponential exothermic model of several types of mineral-admixed concrete can be obtained by adjusting the parameter fitting to solve the influence coefficient of the exothermic hydration model proposed in this paper. (2) One can adjust the influencing coefficient a to control the maximum temperature of concrete. Similarly, the temperature curve of concrete can be adjusted by modifying the influencing coefficient b.

Prediction of Buildings’ Settlement Induced by Metro Station Deep Foundation Pit Construction

The construction of deep foundation pits in subway stations can affect the settlement of existing buildings adjacent to the pits to varying degrees. In this paper, the Long Short-Term Memory neural network prediction model of building settlement caused by deep foundation pit construction was established using the monitoring data of building settlement around a deep foundation pit project in a metro station in Shanghai, and appropriate hyperparameters including batch size and training set ratio were determined. The accuracy of settlement prediction for single-point and multi-point monitoring of buildings was analyzed. Meanwhile, the effects of construction parameters, engineering geological parameters, and spatial parameters on the accuracy of building settlement prediction were investigated. The results show that the batch size and training set proportion can be taken as 16 and 60%, respectively, when using the Long Short-Term Memory neural network prediction model. The proposed Long Short-Term Memory network model can stably predict the settlement of buildings adjacent to deep foundation pits. The accuracy of settlement prediction at a single point of a building (80%) is lower than the accuracy of coordinated prediction at multiple points (88%). More accurate settlement prediction is achieved with the total reverse construction method. The more detailed the consideration of working conditions, geological parameters, and spatial parameters, the better. The evaluation metrics of the prediction model, RMSE, MAE, and R2, were 0.57 mm, 0.65 mm, and 0.91, respectively. The results of this paper have some practical reference value for analyzing the settlement of buildings caused by foundation pit works.

Structural displacement and deformation of adjacent shield tunnel induced by foundation pit excavation

Excavating a foundation pit close to a tunnel will unavoidably impact the nearby soil, causing the tunnel to shift and deform. Examining the longitudinal displacement and convergence deformation of a nearby shield tunnel during foundation pit excavation is crucial for maintaining safe operations. Nevertheless, most of the current research primarily focuses on the effects of a single foundation pit excavation on existing tunnels. This study examines and analyzes the effects of multiple foundation pit excavations on the displacement and deformation of pre-existing tunnels, using a project at a subway station as a case study. By employing the PLAXIS three-dimensional finite element software, a large-scale computational model based on the HS model is established, and then five dangerous working conditions are selected for simulation analysis. The research findings delineate that: (i) The displacement of the tunnel is affected by various factors. These encompass the magnitude of the foundation pit excavation, the positioning of the foundation pit excavation in relation to the tunnel, and the closeness of the tunnel to the foundation pit. (ii) The deformation encountered by the tunnel is largely influenced by external loads, where the scale of the unloading load primarily determines the deformation characteristics of the tunnel.

Analysis of the Impact of Shield Tunneling Under Existing Subway Station

To ensure the safety of existing subway station structures and the normal operation of the metro, this study focuses on a specific engineering case in Suzhou’s metro, where shield tunnels were initiated at a close distance to pass beneath an existing subway station. Numerical simulation method was employed to analysis the impact of shield tunneling parameters on the deformation of the existing station structure under conditions with and without pre-reinforcement boundaries. Additionally, the study involved a comparison with monitoring data to validate the reliability of the numerical simulation approach. The results indicate that: 1) Controlling the shield tunneling parameters, such as setting appropriate shield tunneling face support force and tail grouting pressure, is beneficial in managing the deformation of the existing station; 2) The elastic modulus of an equivalent grouting layer has the greatest impact on settlement. Choosing the appropriate grouting material during construction is a key approach to control settlement. 3) Strengthening the soil strength in the reinforcement area and increasing the thickness of the track slab have a minimal impact on the settlement of the track slab at the existing station. However, the former is beneficial for the stability of the shield excavation face. The latter enhances the existing structure’s resistance to deformation.

Development of A Structural Equation Model of The Variables Affecting the Organizational Performance of The Mass Rapid Transit Systems of Thailand

Introduction: The development of the Thai transport system in the future must be based on the advancement of three aspects: personnel, technology, and research and development must be promoted for the operation of efficient innovative tools and management as key mechanisms in the transport system’s development. The master plan of the Ministry of Transport under Strategy 5 for the development of the Thai transport system within 20 years (2017-2036) stated about the need for and initiation of technology and innovation, as well as the promotion of research and development for the application of rapid advancements within these aspects so to develop the transport infrastructure and management, which would engender higher efficiency of future transport as well. Technology is required and is suitable for the analysis and evaluation of performance in the view of safety technological innovation for subway station construction. Objective: The main objective was to explore the development, congruence, and the indirect direct, and total effects of the variables affecting the organizational performance of the mass rapid transit systems of Thailand Method: This is quantitative research, collecting data from 385 executives and chiefs of organizations relating to the mass railway transit systems and private manufacturers participating in the project with state enterprise agencies. Result: Relationship between the two variables and among the multiple variables in conjunction with the estimation of the effects of the variables, which were obtained from the regression coefficients (coef.) of each individual relationship path following the hypothesis. The results indicated that all C.R. ( t-test) values were statistically significant ( > 1.96). Thus, every hypothesis was supported. Technology directly affects organizational performance (coef. = 0.143, p < 0.05). Technology directly affects innovation (coef. = 0.370, p < 0.001). Technology directly affects research and development (coef. = 0.786, p < 0.001). Research and development directly affects innovation (coef. = 0.624, p < 0.001). Research and development directly affects organizational performance (coef. = 0.449, p < 0.001). Innovation directly affects organizational performance (coef. = 0.372, p < 0.01). Conclusion: Technology, innovation, and research and development truly affect organizational performance. The industrial sector should be more encouraged to participate in technology learning and to step into the manufacturing chain for the development of Thailand’s rail transport system. This would also save foreign currency, create employment opportunities in the country, and have continual economic effects for the national monetary circulation, which would eventually pave the way to the enhancement as well as development of the people’s abilities in the future.

A Novel Spatial–Temporal Deep Learning Method for Metro Flow Prediction Considering External Factors and Periodicity

Predicting metro traffic flow is crucial for efficient urban planning and transit management. It enables cities to optimize resource allocation, reduce congestion, and enhance the overall commuter experience in rapidly urbanizing environments. Nevertheless, metro flow prediction is challenging due to the intricate spatial–temporal relationships inherent in the data and the varying influence of external factors. To model spatial–temporal correlations considering external factors, a novel spatial–temporal deep learning framework is proposed in this study. Firstly, mutual information is utilized to select the highly corrected stations of the examined station. Compared with the traditional correlation calculation methods, mutual information is particularly advantageous for analyzing nonlinear metro flow data. Secondly, metro flow data reflecting the historical trends from different time granularities are incorporated. Additionally, the external factor data that influence the metro flow are also considered. Finally, these multiple sources and dimensions of data are combined and fed into the deep neural network to capture the complex correlations of multi-dimensional data. Sufficient experiments are designed and conducted on the real dataset collected from Xi’an subway to verify the effectiveness of the proposed model. Experimental results are comprehensively analyzed according to the POI information around the subway station.

Numerical analysis and improvement strategy of particulate matter concentration in underground subway stations

Particulate matter (PM) is an important air pollutant in subway systems. Exposure to high PM concentrations poses a threat to human health and raises concerns. Piston effect can provide the outdoor fresh air to subway stations through station exits, but it also brings PM from tunnels to platforms when the train enters the station. In this paper, a transient numerical model is developed according to an actual subway station and validated by field tests. The model is used to investigate PM10 concentration at platform induced by train movement. The cumulative PM10 concentration at platform in one day is calculated according to the train schedule. Platform screen door (PSD) with adjustable vents is studied to control the PM10 level at platform and energy saving in 27 cities of five climatic zones in China are discussed. The results show that adjustable vent height should be less than 0.70 m to ensure that PM10 concentrations at platform can be maintained at an acceptable level and the requirement of fresh air can be met. Moreover, energy consumption can be reduced by more than 70 % in different cities compared with the conventional mechanical ventilation system. The optimized adjustable vent can provide an important reference for improving indoor air quality (IAQ) and saving energy consumption of ventilation systems in underground facilities.

Dynamic evacuation path planning for subway station fire based on IACO

Fire in subway stations is the most serious type of accident causing casualties, and traditional static evacuation paths are no longer able to guarantee evacuation safety. To solve this problem, this study proposes an improved ant colony algorithm (IACO), which dynamically adjusts the evacuation paths by combining the simulation results of simulation software (PyroSim), and extends the available safe evacuation time of the personnel in a limited way. First, PyroSim is used to simulate the subway station hall and platform fire scenarios with the temperature, carbon monoxide (CO) concentration, and visibility at each location, obtained from twelve sets of monitors. Improvements in the pheromone concentration and update strategy of the ant colony algorithm (ACO) can improve the accuracy and convergence speed of the algorithm. Optimal path planning is achieved by the IACO, and then evacuation paths are dynamically adjusted by comparing the time at which each monitoring point on the path reaches the human hazard thresholds. Finally, until no adjustments can be made to obtain an evacuation path that extends the available safe evacuation time for personnel in a limited way. Compared to the traditional static evacuation path, this study provides a dynamic evacuation path that takes into account the real-time changes of fires in subway stations. This dynamic evacuation path planning approach will be put into use in subway stations before the accident to provide guidance on fire safety in subway stations and to provide contingency plans for emergency evacuation.

Data-driven mathematical simulation analysis of emergency evacuation time in smart station's operations management.

This research establishes an emergency evacuation time model specifically designed for subway stations with complex structures. The model takes into account multiple factors, including passenger flow rate, subway facility parameters, and crowd density, to accurately assess evacuation times. It considers the impact of horizontal walking distance, flow rate, subway train size, and stair parameters on the overall evacuation process. By identifying bottleneck points such as gates, car doors, and stairs, the model facilitates the evaluation of evacuation capacity and the formulation of effective evacuation plans, particularly in multiline subway transfer stations. The good consistency is achieved between the calculated evacuation time and simulated results using the Pathfinder software (with the relative error of 5.4%). To address urban traffic congestion and enhance subway station safety, the study recommends implemented measures for emergency diversion and passenger flow control. Additionally, the research presents characteristic mathematical models for various evacuation routes by considering the structural and temporal characteristics of metro systems. These models provide valuable guidance for conducting large-scale passenger evacuation simulations in complex environments. Future research can further enhance the model by incorporating psychological factors, evacuation signage, and strategies for vulnerable populations. Overall, this study contributes to a better understanding of evacuation dynamics and provides practical insights to improve safety and efficiency in subway systems.

A decision-aid system for subway microenvironment health risk intervention based on backpropagation neural network and permutation feature importance method

Subway has become an important commute mode in daily life. However, subway microenvironment poses a threat to people's health and there is growing concern about its health risks. In this paper, a decision-aid system for subway microenvironment health risk intervention was proposed based on the combination of backpropagation neural network algorithm and permutation feature importance method. The non-linear relationship between indicators and the risks of subway microenvironment was established using the neural network. On this basis, the implementation of stepwise intervention, neural network prediction, and the generation of strategies was achieved utilizing MATLAB programming. Moreover, a subway station in Nanjing was taken as an example to illustrate the implementation and verify the effectiveness of the system. With the help of the system, health risks in the subway microenvironment can be systematically analyzed, efficiently optimized, and gradually solved. Our research contributes to breaking the deadlock in subway microenvironment intervention, which has long been confined to predicting and summarizing strategies. It provides more objective and effective intervention strategies for the health risk management of subway microenvironments.