Metro Station Research Articles

Research on Passenger Flow Control Plans for a Metro Station Based on Social Force Model

To better utilise the service capacity of the limited facilities of a metro station, as well as ensure safety and transport efficiency during peak hours, a large passenger flow control plan is studied through theoretical analysis and numerical simulation. Firstly, by passenger data collection and data analysis, the characteristics of the inbound and outbound passenger flow of a T metro station are analysed. Secondly, AnyLogic evacuation simulation models for the T Station during peak hours, peak hours without/with passenger flow control are established based on real passenger flow data as well as the station structures and layouts by using the AnyLogic software. The results show that there are no obvious congestions in the station hall, and the travel delay is significantly reduced when effective passenger flow control measures are taken. By controlling the speed, direction and movement path of passengers, as well as adjusting the operation of escalators, entrances and automatic ticket-checking machines, passenger flow can become more orderly, transport efficiency can also be improved, and congestion in the station can be well mitigated.

Transverse - longitudinal flexural performance of steel tube slab composite slabs: A novel pipe roofing system for metro station

The steel tube slab (STS) structure is a novel pipe-roofing structure, which is used as the support system for underground stations. The pipe-roofing structure is generally assumed as a beam structure in previous research, which is questionable, because the transverse and longitudinal coupling force mechanism is ignored. Hence, this work aims to investigate the flexural performance of STS composite slab through experimental test and establish a design approach for STS structure. Firstly, based on six tests on flexural performance of the STS composite slabs, the crack development, failure mode and deformation mechanism were analysed. Each of the specimens demonstrated a high bearing capacity that exceeded 3000 kN. Furthermore, the specimens exhibited ductility during testing with the failure occurring at either the flange or concrete between the steel tubes. Subsequently, the effect of the length–width ratio and spacing between the tubes on the flexural performance of the tested specimens were examined. The result indicated that the length–width ratio and the spacing between the tubes significantly affected the flexural stiffness, but had limited effects on the flexural capacity. Finally, a design approach was proposed to estimate the elastic capacity of STS composite slabs, and finite element (FE) models validated by the test results were established to increase the general applicability of the theoretical model. The predicted outcomes demonstrated a satisfactory correlation with both the test and FE results, with an average relative error of 9.8%. The study's findings are anticipated to serve as a dependable resource for the design and application of STS structures.

Association of the Built Environment with Residents’ Car Dependence: Evidence from Shenzhen, China

Reducing car dependence is the key to achieving the goal of green and sustainable development. Compared with the existing studies, which mainly focus on administrative areas, this study takes residential areas as the research unit. Four spatial regression models were used to investigate the effect on car dependence of six factors of the built environment (land use mix, population density, jobs–housing balance, bus stop density, metro station density, and road network density). Various test results show that the geography-weighted regression (GWR) model has more substantial explanatory power and that the estimated coefficients of built environment characteristics vary positively or negatively in diverse residential communities. The findings demonstrate that the impact of built environment characteristics on car dependence is significantly spatially heterogeneous. These results are conducive to better comprehending how built environment factors affect car dependence and help establish policies and strategies to promote sustainable transportation.

Modified stochastic medium prediction model for the deformation response of concealed underground stations under existing pipelines

The underground pipeline network in the city is so intertwined that the concealed excavation of a metro station inevitably leads to a series of underground pipelines, causing settlement deformation and further risk of leakage. The existing theoretical methods for analysing settlement deformation are mostly for circular chambers, whereas metro stations have a nearly square cross-sectional form and different construction methods are very different, which have a greater impact on the deformation of the overlying pipelines. In this paper, based on the random medium theory and Peck's formula, the improved random medium model for predicting ground deformation is modified, the correction coefficients λ and η for the influence of different construction methods are proposed, the prediction model of underground pipeline deformation under different construction methods is obtained, and the numerical models of four work methods commonly used in urban tunnel construction: pillar hole method, side hole method, middle hole method and Pile-Beam-Arch (PBA) method are constructed through simulation, and the mathematical analysis software was used to fit the results to the model and obtain the range of correction coefficients λ and η for each of the four methods, and the accuracy and applicability of the theoretical model was verified by combining with actual engineering cases. The influence on the overlying pipes is in descending order: side hole method, pillar hole method, middle hole method and PBA method. The theoretical model provided in this paper for predicting the deformation of pipes in any overlying strata of the tunnel is well suited to the actual project and has a high degree of correlation with the measured results.

A Segmental Support Method for the Passage of TBMs Through Excavated Metro Stations

A Segmental Support Method for the Passage of TBMs Through Excavated Metro Stations

Research of Metro Stations with Varying Patterns of Ridership and Their Relationship with Built Environment, on the Example of Tianjin, China

The metro station ridership features are associated significantly with the built environment factors of the pedestrian catchment area surrounding metro stations. The existing studies have focused on the impact on total ridership at metro stations, ignoring the impact on varying patterns of metro station ridership. Therefore, the reasonable identification of metro station categories and built environment factors affecting the varying patterns of ridership in different categories of stations is very important for metro construction. In this study, we developed a data-driven framework to examine the relationship between varying patterns of metro station ridership and built environment factors in these areas. By leveraging smart card data, we extracted the dynamic characteristics of ridership and utilized hierarchical clustering and K-means clustering to identify diverse patterns of metro station ridership, and we finally identified six main ridership patterns. We then developed a newly built environment measurement framework and adopted multinomial logistic regression analysis to explore the association between ridership patterns and built environment factors. (1) The clustering analysis results revealed that six station types were classified based on varying patterns of passenger flow, representing distinct functional characteristics. (2) The regression analysis indicated that diversity, density, and location factors were significantly associated with most station function types, while destination accessibility was only positively associated with employment-oriented type stations, and centrality was only associated with employment-oriented hybrid type station. The research results could inform the spatial planning and design around metro stations and the planning and design of metro systems. The built environment of pedestrian catchment areas surrounding metro stations can be enhanced through rational land use planning and the appropriate allocation of urban infrastructure and public service facilities.

Flood risk assessment and mitigation for metro stations: An evidential-reasoning-based optimality approach considering uncertainty of subjective parameters

This study presents an optimality approach based on evidential reasoning for flood risk assessment and mitigation of metro stations. To address the uncertainty of subjective parameters involved in risk assessment, we consider the subjective parameters to vary within certain intervals, and then employ evolutionary optimization to solve the maximum risk expectation, which is adopted as the index to measure the flood risk in the worst case under the uncertainty of subjective parameters. Furthermore, we propose a sensitivity indicator termed combined gradient to identify critical influential factors and correspondingly provide risk mitigation measures for the high-risk metro stations. A case study is conducted on Shanghai metro stations to verify the applicability and validity of the proposed approach. The results mainly indicate that: (1) The metro stations in eastern Shanghai have higher flood risks than those in central and western areas. (2) Improving the drainage capacity is of uppermost priority for flood risk mitigation. The novelty of the evidential-reasoning-based optimality approach lies in its capabilities of (1) considering the uncertainty of all kinds of subjective parameters in a unified framework; (2) considering both the short-term and long-term benefits of risk mitigation measures so that the most effective measures can be suggested.

On the positioning of emergencies detection units based on geospatial data of urban response centres

Urban areas have been subject to emergency situations with different causes and sometimes dramatic consequences. With the advent of smart cities technologies, multi-emergency detection units could be conceived and implemented taking advantage of affordable sensing technologies and efficient decision algorithms, allowing quick and distributed detection of emergencies. However, a recurrent problem has been the positioning and further deployment of such detection units in a way that the particularities of each target city are properly considered. In this sense, this article proposes the processing of geospatial data about existing urban infrastructure associated with some critical response after an emergency is detected, selecting hospitals, police stations, fire departments, and metro stations, in the target city. These infrastructures are then exploited to define the novel concept of mitigation zones, which indirectly express the perceived level of urban resilience to emergencies. Then, four positioning algorithms are proposed to exploit the mitigation zones considering a defined set of available detection units for deployment. Since the proposed approach is valid for any city, provided that geospatial data is available, it could be largely adopted to support different smart city systems, potentially bringing significant results in this area.

Clustering Analysis of Multilayer Complex Network of Nanjing Metro Based on Traffic Line and Passenger Flow Big Data

Complex networks in reality are not just single-layer networks. The connection of nodes in an urban metro network includes two kinds of connections: line and passenger flow. In fact, it is a multilayer network. The line network constructed by the Space L model based on a complex network reflects the geographical proximity of stations, which is an undirected and weightless network. The passenger flow network constructed with smart card big data reflects the passenger flow relationship between stations, which is a directed weighted network. The construction of a line-flow multilayer network can reflect the actual situation of metro traffic passenger flow, and the node clustering coefficient can measure the passenger flow clustering effect of the station on adjacent stations. Combined with the situation of subway lines in Nanjing and card-swiping big data, this research constructs the line network with the Space L model and the passenger flow network with smart card big data, and uses these two networks to construct the multilayer network of line flow. This research improves the calculation method of the clustering coefficient of weighted networks, proposes the concept of node group, distinguishes the inflow and outflow, and successively calculates the clustering coefficient of nodes and the whole network in the multilayer network. The degree of passenger flow activity in the network thermal diagram is used to represent the passenger flow activity of the line-flow network. This method can be used to evaluate the clustering effect of metro stations and identify the business districts in the metro network, so as to improve the level of intelligent transportation management and provide a theoretical basis for transportation construction and business planning.

Real‐time passenger flow anomaly detection in metro system

AbstractReal‐time passenger‐flow anomaly detection at all metro stations is a very critical task for advanced Internet management. Robust principal component analysis (RPCA) based method has often been employed for anomaly detection task of multivariate time series data. However, it ignores the spatio‐temporal features of regular passenger‐flow patterns, resulting in a decrease in the accuracy of anomaly detection. In this paper, RT‐STRPCA model integrating temporal periodicity and spatial similarity is proposed to address the above issues. RT‐STRPCA model detects anomalies by decomposing the observation data into normal component and anomaly component. The spatio‐temporal constraints are taken into account to extract anomalies more accurately. The real‐time anomaly detection are realized by a sliding window. The performance of RT‐STRPCA model is evaluated on synthetic datasets and real‐world datasets. The experimental results on synthetic datasets demonstrate that the method achieves more accurate real‐time anomaly detection than baseline approaches and the result on real‐world datasets verify the utility and effectiveness of the proposed method.

An automated health monitoring system for uncoordinated deformation between the metro station side wall and row piles

An automated health monitoring system for uncoordinated deformation between the metro station side wall and row piles

Relationship between motorcycle travel and the built environment: Evidence from Taipei, Taiwan

The current understanding of the built environment effects on motorcycle ownership is limited. No built environment characteristics have been identified as predictors of motorcycle mode choice likelihood. This research fills these gaps in knowledge by examining the relationship between motorcycle travel and the built environment using the largest metropolis of Taiwan, home to the world's highest motorcycle ownership rate, as the study context. Multinomial logistic and tobit regression models were estimated to examine the relationship between the built environment on household vehicle ownership, mode choice in a trip, and household motorcycle vehicle kilometers traveled (VKT), controlling for socioeconomic predictors of motorcycle travel. It used the most recent trip and household datasets from both the 2000 and 2009 large-scale household travel surveys in the Taipei Metropolitan Area for analysis. Findings suggest that higher population density, being farther from the central business district and from metro stations are correlated with higher household motorcycle ownership levels, higher likelihood of motorcycle mode choice, and higher household motorcycle VKT; lower job density is correlated with higher household motorcycle ownership levels and higher household motorcycle VKT; higher income is correlated with lower motorcycle ownership levels, lower motorcycle mode choice likelihood, and higher household motorcycle VKT. The academic novelty of this research is that it rejects most of its literature-based hypotheses about motorcycle travel and the built environment. Transportation and land use policy should expand mass transit, develop suburban employment centers near metro stations, and reshape the spatial distribution of population so that areas near metro stations become more densely populated than others.

Rethinking urbanism in Doha : The Qatar National Museum transit-oriented development

The urban environment of Doha has witnessed a remarkable transformation during the past few decades, which has been triggered by economic growth due to oil and gas revenues. Doha has attracted foreign investment to participate in the urbanisation of the city, specifically in the construction of the smart public transport system (ie Bus Transit [BT], Lusail Light Rail Transit [LRT] and the Doha Metro). As a result, mega projects and transit villages were developed along the public transport system, especially around the major metro stations. These developments align with the national development strategy defined in the Qatar National Vision 2030 (QNV-2030). This research investigates the development and implementation of transit villages around the transit systems (also known as the transit-oriented development [TOD]) at the Qatar National Museum (QNM) metro station in Old Salata neighbourhood. The research design focuses on: (i) a review of the relevant literature; (ii) the site analysis; and (iii) the generation of an integrated strategy for the development of the transit village of Old Salata. The proposed master plan for QNM TOD is supported by the need for preserving the architectural heritage, through a compact and mixed-use urban form, where multimodal transport systems, green spaces, public amenities and a pedestrian-friendly environment are provided. In turn, the insights from this investigation will contribute to the implementation of an urban planning approach for shaping a liveable community in Doha.

That Anti-racist Feeling

Abstract This article traces the devolution of Soviet anti-racism and the emergence of ethnonationalist violence amid the collapse of the Soviet Union. Through an analysis of Uzbek writer Hamid Ismailov’s novel Mbobo/The Underground (2009), it explores the contradictions of Soviet anti-racism at the interface of flesh and place, metaphor and materiality, ecology and affect—contradictions manifested in the ways in which Brown and Black bodies were mapped onto the triumphalist architecture of socialist internationalism. Attending to built infrastructures—metro stations, sports arenas, concert halls, and conference venues—and the bodies of visibly marked internal and international others who constructed, inhabited and moved through these spaces, it discusses how these bodies were conscripted in the material manifestation of socialist internationalism and then made the targets of racialized violence in the waning days of the Soviet Union.

Data-driven interpretation on interactive and nonlinear effects of the correlated built environment on shared mobility

Understanding the usage demand of shared mobility systems in different areas of a city and its determinants is crucial for planning, operation and management of the systems. This study leverages an unbiased data-driven approach called accumulated effect analysis for examining the complex (nonlinear and interactive) effects of correlated built environment factors on the usage of shared mobility. Special research emphasis is given to unraveling the complex effects using an unbiased and data-driven approach that can overcome the impacts of correlations among built environment factors. Based on empirical analysis of synthetic data and a field dataset about dockless bike sharing systems (DLBS), results demonstrate that the method of partial dependency analysis prevalent in the relevant literature, will result in biases when investigating the effects of correlated built environment factors. In comparison, accumulated local effect analysis can appropriately interpret the effects of correlated built environment factors. The main effects of many built environment factors on the usage of DLBS present nonlinear and threshold patterns, quantitively revealed by accumulated local analysis. The approach can reveal complex interaction effects between different built environment factors (e.g., commercial service and education facility, and metro station coverage and living facility) on the usage of DLBS as well. The interactions among two built environment factors could even change with the values of the factors rather than invariant. The outcomes offer a new approach for revealing complex influences of different built environment factors with correlations as well as in-depth empirical understandings regarding the usage of DLBS.

Improving of Interaction of Routes at High Density Stops by Adjusting Bus Arrival Time

Abstract A significant part of the movement of the population in large cities is carried out by bus transport. Higher accessibility and connection to the network of other types of public transport are the main features of bus transport. The need for mass passenger transportation in Baku mainly is met by the metro and bus transport. A small number of metro stations and insufficient coverage of the city territory by metro network have significantly increased the load on the bus service. With the increase in passenger traffic, the frequency of bus routes has increased, and the intervals have decreased. As a result, bus traffic on some streets has become more intense. This led to an increase in the number of buses arriving at the stops at the same time and the formation of queues in front of the stops. To solve the problem, the article proposes a method for coordinating bus schedules of different routes and adjusting the time of arrival at a stop without changing the intervals. As a result of applying the proposed methodology, by modelling the sequence of bus arrivals in the Anylogic program, a reduction in queues and loss of time in front of the bus stop is justified. Simulation testing was carried out for the scenario of bus movement both in the general flow and along the bus lane.

Settlement Prediction due to Diaphragm Walls Installation at Heliopolis Metro Station in Cairo, Egypt

Settlement Prediction due to Diaphragm Walls Installation at Heliopolis Metro Station in Cairo, Egypt

Prediction of GHG emissions from Chengdu Metro in the construction stage based on WOA-DELM

With the mass construction of urban subways, the global greenhouse gas (GHG) emissions have been on the rise. This paper provides statistical evidence to support the infrastructure of subway emissions reduction through a study of GHG emissions during the construction stage of 6 stations and 7 sections of Chengdu Metro Line 18. Using the emission coefficient method, the GHG emissions from building material production, transportation and site construction in subway stations and shield sections were calculated, and a subway GHG emissions prediction model dependent on deep extreme learning machine (DELM) with whale optimization algorithm (WOA) was established(i.e., WOA-DELM). Compared with some optimized DELMs, namely wind driven optimizer (WDO) -DELM, grey wolf optimizer (GWO) -DELM, particle swarm optimizer (PSO) -DELM, artificial bee colony (ABC) -DELM, multi verse optimizer (MVO) -DELM, and atom search optimizer (ASO) -DELM, and some non-optimized algorithm models, namely back propagation neural network (BPNN), kernel extreme learning machine (KELM) and DELM, the correlation consistency of WOA-DELM algorithm prediction results (0.757) was found to be slightly higher. Through sensitivity analysis of the main input variables of subway GHG emissions with the WOA-DELM algorithm model, it was determined that the key influencing factors of station GHG emissions prediction were the station length and the depth of track surface, with relative change rates of corresponding variables of GHG emissions at 30.1% and 23.1% respectively. Finally, a rough prediction formula of GHG emissions from Chengdu Metro stations and shield sections were fitted based on the key influencing factors of GHG emissions. This study provides a practical and effective reference for reducing GHG emissions in subway construction and operation.

Identifying Critical Stations Affecting Vulnerability of a Metro Network Considering Passenger Flow and Cascading Failure: Case of Xi’an Metro in China

Analyzing the importance grade of each metro station is the premise and foundation to ensure the safe and efficient operation of the metro network. This paper explores the identification method of critical stations in the metro network on the basis of considering the influence of passenger flow. Eight indicators were selected from both the static and dynamic aspects to construct the critical station identification index system considering the impact of passenger flow and cascading failure. Then, the triangular fuzzy analytic hierarchy process (TFAHP) method and critical importance through intercriteria correlation (CRITIC) method were used to calculate subjective and objective weight of all indicators, and the combined weighting method based on game theory was applied to calculate the comprehensive weight. The improved technique for order of preference by similarity to ideal solution (TOPSIS) method was developed to calculate the station importance and rank all stations according to their importance. Finally, the Xi’an metro network by mid-2021 was taken as the case study. The results show that there are 21 critical stations in the Xi’an metro network in three time periods (i.e., morning rush hours, evening rush hours, and nonrush hours) on weekdays and weekends, respectively, and those critical stations are divided into three levels. High centrality, high passenger flow intensity, and strong destructive ability are three important characteristics of critical stations in the metro network, and different critical stations have different importance characteristics. The Xi’an metro operation management authority needs to conduct a classification management scheme for those critical stations. The conclusions can provide the basis for the metro operation management authority to formulate accurate station management scheme, so as to increase the robustness of the metro network.

Occupational exposure and personal exposure to hazardous air pollutants in underground metro stations and factors causing poor indoor air quality

Occupational exposure and personal exposure to hazardous air pollutants in underground metro stations and factors causing poor indoor air quality