Urban Metro Systems Research Articles

Scenario inference model of urban metro system cascading failure under extreme rainfall conditions

Metro systems have become high-vulnerability entities due to the increasing frequency and severity of urban flooding. Flood events may cause cascading failure to metro systems; therefore, exploring the cascading failure risk of the metro system is a prerequisite for urban flooding prevention and risk management. This study presented a Rank-Order Centroid (ROC) based CIA-ISM (Cross-Impact Analysis, and Interpretive Structural Modeling) method to accurately assess the reliability of emergency management in metro systems under extreme rainfall conditions. We applied this approach to a metro flooding case in Zhengzhou on July 20, 2021. The reliability results show that efficient rescue and timely shutdown notification are the most critical causal events in the cascading failure scenarios. The events of system vulnerability that have the most significant impact on casualties, property losses, and social panic are, respectively, timely notification of the shutdown, humanitarian aid, and public opinion guidance. In forecast scenarios with emergency management measures in effect, the probability of casualties, property losses, and social panic decrease by 96.3%, 58.58%, and 64.28%, respectively. Moreover, a comparison with Bayesian Network (BN) model verified the effectiveness of the ROC-based CIA-ISM approach. Based on the study, we suggest the metro companies release a timely notification of the shutdown. This study can provide scientific data for decision-makers to reasonably develop emergency strategies, significantly reducing flood losses and promoting cities’ sustainable development.

Detecting metro service disruptions via large-scale vehicle location data

Urban metro systems are often affected by disruptions such as infrastructure malfunctions, rolling stock breakdowns and accidents. The crucial prerequisite of any disruption analytics is to have accurate information about the location, occurrence time, duration and propagation of disruptions. To pursue this goal, we detect the abnormal deviations in trains’ headway relative to their regular services by using Gaussian mixture models. Our method is a unique contribution in the sense that it proposes a novel, probabilistic, unsupervised clustering framework and it can effectively detect any type of service interruptions, including minor delays of just a few minutes. In contrast to traditional manual inspections and other detection methods based on social media data or smart card data, which suffer from human errors, limited monitoring coverage, and potential bias, our approach uses information on train trajectories derived from automated vehicle location (train movement) data. As an important research output, this paper delivers innovative analyses of the propagation progress of disruptions along metro lines, which enables us to distinguish primary and secondary disruptions as well as effective recovery interventions performed by operators.

Resilience Assessment of an Urban Metro Complex Network: A Case Study of the Zhengzhou Metro

An urban metro network is susceptible to becoming vulnerable and difficult to recover quickly in the face of an unexpected attack on account of the system’s complexity and the threat of various emergencies. Therefore, it is necessary to assess the resilience of urban metro networks. However, the research on resilience assessment of urban metro networks is still in the development stage, and it is better to conduct said research using a technique which combines many attributes, multiple methods, and several cases. Therefore, based on the complex network modeling and topological characteristics analysis of metro systems, a metro network’s robustness and vulnerability measurement method under node interruption and edge failure is proposed for the first time in this study. Then, considering the three cases of general station interruption, interchange station interruption, and traffic tunnel failure, a quantitative resilience assessment model of metro networks is put forward, and the corresponding recovery strategies are discussed. Finally, a case study of the Zhengzhou Metro Network (ZZMN) under an extreme rainstorm is conducted to demonstrate the viability of the proposed model. The results show that ZZMN possesses scale-free and small-world network properties, and it is robust to random interruptions but vulnerable to deliberate attacks. ZZMN still needs to improve its effectiveness in information transmission. The centrality distribution for each node in the ZZMN network differs significantly, and each node’s failure has a unique impact on the network. The larger the DC, BC, and PR of a node is, the lower the network’s robustness after its removal is, and the stronger the vulnerability is. Compared with the three cases of general station interruption, interchange station interruption, and traffic tunnel failure, the network loss caused by tunnel failure was the lowest, followed by general station interruption, and the interruption at interchange stations was the most costly. Given the failures under various cases, the metro management department should prioritize selecting the optimal recovery strategy to improve the resilience of the metro network system. This study’s findings can assist in making urban metro systems less vulnerable to emergencies and more resilient for a quick recovery, which can provide scientific theoretical guidance and decision support for the safety and resilient, sustainable development of urban metro systems.

Mechanical Responses of Slurry Shield Underpassing Existing Bridge Piles in Upper-Soft and Lower-Hard Composite Strata

With the development of urban metro systems, shield tunnels that pass through existing bridge pile foundations have become an inevitable engineering problem. Therefore, ensuring the stability of the strata and existing bridge piles during tunnel construction is a common goal in engineering practice. Currently, research on the mechanical responses of strata and existing piles under different conditions of upper-soft and lower-hard composite strata during shield tunneling has not been conducted extensively. This paper presents a numerical simulation of a shield tunnel passing through an existing bridge pile foundation in upper-soft and lower-hard composite strata. Subsequently, the surface subsidence and mechanical responses of a single pile were analyzed and evaluated. Additional stresses generated in the soil by existing bridge piles and the selection of grouting pressure were considered to optimize the driving pressure of the slurry shield. Allowable values were proposed to evaluate the construction disturbances caused by the tunnel excavation. The results show that the disturbance to the soil and existing piles is significantly influenced by the hard-rock height ratio, and the surface subsidence increases when the hard-rock height ratio decreases. The displacement and internal force of a single pile are significantly influenced by the load applied to the pile. This study demonstrates the changes in the mechanical responses of a single pile during shield tunnel boring, and provides in-depth insights into the deformation control caused by shield underpassing structures in upper-soft and lower-hard composite strata.

First-Train Timetable Synchronization in Metro Networks under Origin-Destination Demand Conditions

This paper focuses on how to synchronize network-wide timetables of first trains in an urban metro system, in which the train-connection-based route can be exactly determined for each first-train-attached origin-destination (OD) demand pair. With the help of even headway scheduling on each line, the problem is actually to adjust the departure times of first trains and connecting trains from their origin stations and the departure interval on each line. Subjected to train operation and connection constraints, a biobjective nonlinear integer programming model is formulated to minimize the total travel time of OD-dependent passenger demands and the deviation between the known and expected schedules. Then, the Nondominated Sorted Genetic Algorithm-II (NSGA-II) is adopted to solve the proposed model, and an improved technique is elaborated to reduce the alternative route choices. Finally, numerical experiments are conducted to demonstrate the effectiveness and availability of the proposed model and methods.

Low-Frequency Vibration Control of Metro Slab Track Based on Locally Resonant Theory

Vibration control has always been one of the research hotspots in the field of urban metro system. Due to the unsatisfactory application effect of traditional vibration damping technology in practice, a novel method, based on the locally resonant theory, was introduced into metro vibration damping system to improve the low-frequency vibration reduction effect. Meanwhile, the new metro vibration reduction slab track was designed. Furthermore, the coupled model of the metro vehicle-slab track was established and verified, and the reduction effect of the new metro slab track on low-frequency vibration under the action of metro vehicles was analyzed. Results show that: 1) The new vibration reduction slab track is really in control of the low-frequency vibration (50–100 Hz), and the attenuation effect can reach 10 dB; 2) The new slab track has multiple low-frequency vibration band gaps, which can achieve multi-frequency vibration reduction; 3) The new slab track can reduce the vibration acceleration of the track, which can improve the comfort level of the vehicle. Therefore, the local resonance theory enriches the existing metro vibration reduction design theory and provides a method for the design of the metro vibration control.

Quantifying out-of-station waiting time in oversaturated urban metro systems

Metro systems in megacities such as Beijing, Shenzhen, and Guangzhou are under great passenger demand pressure. During peak hours, it is common to see oversaturated conditions (i.e., passenger demand exceeds network capacity) and a popular control intervention is to restrict the entering rate by setting up out-of-station queueing with crowd control barriers. The out-of-station waiting can make up a substantial proportion of total travel time but is often ignored in the literature. Quantifying out-of-station waiting is important to evaluating the social benefit and cost of metro services; however, out-of-station waiting is difficult to estimate because it leaves no trace in smart card transactions of metros. In this study, we estimate the out-of-station waiting time by leveraging the information from a small group of transfer passengers—those who transfer from nearby bus routes to the metro station. Based on the transfer interval of this small group, we infer the out-of-station waiting time for all passengers by a Gaussian Process regression and then use the estimated out-of-station waiting time to build queueing diagrams. We apply our method to the Tiantongyuan North station of Beijing metro; results show that the maximum out-of-station waiting time can reach 15 ​min, and the maximum queue length can be over 3000 passengers. We find out-of-station waiting can cause significant travel costs and thus should be considered in analyzing transit performance, mode choice, and social benefits. To the best of our knowledge, this paper is the first quantitative study for out-of-station waiting time.

Promoting the integrated use of bikeshare and metro: A focus on the nonlinearity of built environment effects

Bikeshare offers a flexible feeder mode to metro and improves the overall connectivity of urban public transport systems. Although bikeshare has received much research attention, the relationship between the built environment and bikeshare-metro integrated use (i.e. the use of bikeshare for the first/last mile) remains underexplored. Using a one-month dataset of bikeshare trip records in Nanjing, China, this research scrutinizes the built environment correlates of the integrated use. A generalized additive mixed model is employed to capture the temporal autocorrelation attributable to repeated observations over time and the spatial autocorrelation resulting from geographical proximity between individual metro stations. The results show that built environment variables do impose salient nonlinear effects on the bikeshare-metro integrated use. For example, population density only increases the integrated use at certain intervals, and interestingly, extremely high density leads to a decline in bikeshare-metro integrated use. The proportion of commercial and greenspace land uses within metro station catchment areas should not be too high or too low. There exists an optimal land use setting that maximizes the utility of land-use interventions. These findings provide useful policy implications for developing an environment that facilitates the integration of bikeshare and urban metro systems.

Last Train Delay Management in a Metro System: A Multi-Objective Rescheduling Optimization Model

In urban metro systems, train delays make planned timetables uncoordinated, especially during the end-of-service period. Therefore, this study focuses on the timetable rescheduling problem to improve network accessibility. A multi-objective programming model is first proposed to optimize the headway between the last and penultimate train and station dwell time. Minimization of the passenger transfer waiting time for all origin-destination (OD) pairs is meaningful to improve passenger transport efficiency, and a penalty time is used instead of the transfer waiting time of inaccessible passengers, which is important to decrease inaccessible passenger volume. On the other hand, minimization of the total last-train dwell time for all lines is beneficial for decreasing the operating costs for metro operation companies. Then, the adaptive non-dominated sorting genetic algorithm-II (ANSGA-II) is combined with a label setting method to obtain the Pareto optimal solutions. Finally, the Nanjing metro network with randomly-designed delay scenarios is adopted to verify the effectiveness of the proposed rescheduling model and solution algorithm. The results show that this model can yield rescheduling schemes to address the different sudden delay events, resulting in a significant decrease in passenger transfer waiting time and last-train dwell time, thereby improving the network accessibility.

Vulnerability modeling, assessment, and improvement in urban metro systems: A probabilistic system dynamics approach

The urban metro system is a complex system that is vulnerable to various kinds of hazards and subjected to dynamics, where errors in any part are very likely to cause system failures and accidents. A better understanding of evolutional dynamics of the system vulnerability over time enables to ensure the safety and security in its operation and provide guidance for long-term operation and maintenance for the metro system. However, there are limited studies considering the long-term evolutional dynamics and uncertainties of the vulnerability in urban metro system. To solve the concern, the objective of this research is to develop a hybrid approach that integrates system dynamics (SD) and Monte Carlo (MC) simulation to evaluate the vulnerability of the metro system in operation from a long-term perspective. By merging multi-resource information, an SD model consisting of 4 sub-systems (i.e., workforce vulnerability, organizational vulnerability, equipment vulnerability, and environmental vulnerability) and 16 feedback loops is constructed to reveal complex relationships among various factors and simulate the overall system vulnerability. The MC simulation is used to analyze the sensitive factors and improvement strategies of vulnerability in urban metro systems under uncertainty. The operation network of the Wuhan metro system in China is utilized as a case study to verify the applicability of the proposed approach. Results indicate that (i) It is predicted that the overall system vulnerability will decrease at the early stage of operation and rise at the later stage with the rapid decline of equipment system status; (ii) The workforce sub-system is discovered to be more sensitive to the overall metro system globally than the other three sub-systems, with the 10% and 20% fluctuations in input factors’ variations considered; (iii) The strategy that aims to improve the safety training and equipment maintenance and operation frequency is identified as the most effective strategy to reduce the vulnerability of the overall metro system. The developed approach can be used as a decision tool that is capable of modeling the long-term vulnerability of the overall metro system and discovering appropriate improvement strategies towards more sustainable transit development.

Data-driven optimization for mitigating tunnel-induced damages

Along with the rapid development of urban metro systems, the tunnel-induced damage becomes one of the most critical problems closely related to the safety of tunneling projects. It is urgent to perform an in-depth analysis, identify the key factors influencing the damage, and look for the strategies that could optimize the tunneling process to realize the tunnel-induced damage mitigation. To achieve this, a hybrid data-driven approach with the integration of random forest and non-dominant sorting genetic algorithm-II (NSGA-II) is proposed to perform the multi-objective optimization for mitigating tunnel-induced damages under uncertainty. The random forest is used to construct the meta-model between identified influential factors and objectives. NSGA-II is used to perform the optimization process based on the proposed optimization principle. A total of 16 input variables are identified, and two key factors (i.e., the accumulative settlement and building tilt rate) are determined as the optimization objectives related to the mitigation of the tunnel-induced damage. A case study is conducted to test the applicability and effectiveness of the proposed approach. Through the case study, it is found that: (1) An average damage mitigation improvement degree of 20.9% can be achieved through the proposed optimization process; (2) The optimization can gain the highest improvement degree 32.6% for the tunnel-induced damage mitigation problem when adjusting 3 influential variables; (3) The proposed approach is applicable for the damage mitigation optimization with more objectives, but the consideration of a third objective degrades the optimization improvement for the first two by 2.2% and 6.5%, respectively. The novelty lies in that: (1) The random forest algorithm is incorporated into the model to represent the complex relationship between the identified objectives and the influential factors; (2) Multi-objectives are identified for the mitigation of the tunnel-induced damages, and the optimization of the multi-objectives is realized by the integration of NSGA-II. This research enriches the area of the safety management of tunneling projects by the integration of the random forest and NSGA-II algorithms. With the proposed hybrid approach, the complex relationship between desired objectives and the influential factors could be represented, and the damage mitigation and project optimization could be realized, even potential conflict between objectives may exist.

Analysing travel satisfaction of tourists towards a metro system from unstructured data

Tourism promotes economic development in cities, where the quality of public transport has a direct impact on the attractivity of a destination, making important to understand how to improve the service they provide.This study proposes a methodology to analyse and predict travel satisfaction of tourists towards an urban metro system using online reviews and uses TripAdvisor data and the Paris metro as a case study. The Latent Dirichlet Allocation method is used to extract the dimensions of the online reviews and several machine learning models are trained to find the one that most accurately predicts the tourists' travel satisfaction.According to the results for our case study, TripAdvisor online reviews of tourists towards the Paris metro can be classified in three dimensions: “General”, “Ticketing System” and “Utility and Accessibility”, and an artificial neural network can predict the satisfaction of tourists towards this urban metro system.This methodology can be easily applied to different online platforms and urban metro systems and can be used by metro managements in order to deliver a better service to improve satisfaction among the tourists using their metro systems, and develop strategies aiming to improve the services they provide to attract more tourists to the cities.

Research on Comprehensive Evaluation of the Operation and Maintenance Status of Urban Deep Metro Station

With the rapid development of urban metro system, the operation and maintenance status of urban metro station has received increasing attention. The operation and maintenance status of urban subway stations involve various factors including structure, equipment, and environment, which are difficult to be accurately evaluated directly. On the other hand, as the burial depth of metro station increases, disaster-triggering factors become increasingly complex and diverse, which enhances the difficulty of evaluating the operation and maintenance status. A new method is proposed herein to realize the comprehensive evaluation of the operation and maintenance status of urban deep metro stations. Firstly, the evaluation indexes needed for evaluation are selected by referring to a large number of literatures, combined with many relevant specifications and standards, and based on some existing index selection principles. According to the selected indexes, an evaluation index system of operation and maintenance status of underground metro station is established. Then, based on the established evaluation index system, a comprehensive evaluation method for the operation and maintenance status of urban deep metro station is developed by combining the analytic hierarchy process (AHP) and relevant information aggregation methods. This method is characterized by considering the functions with prominent defects with the use of weighted geometric average (WGA) and variable weight methods. Finally, the proposed evaluation scheme is applied to guide the maintenance of China’s deepest metro station. The proposed comprehensive evaluation method will promote the in-time management and maintenance of urban deep metro station.

Sustainable Development of Urban Metro System: Perspective of Coordination between Supply and Demand.

Metro systems are gradually becoming more and more crucial in promoting the economy and society in cities. However, various challenges such as financial resources and the efficiency of utilizing these metro plans bring difficulties for metro construction. Hence, accurately evaluating the urban metro system’s development condition seems significant for the sustainable development of the urban metro system. Therefore, a comprehensive evaluation indicator system of metro development conditions containing 25 indicators from dimensions of demand and supply is established in this study, and a coupling coordination degree model combined with the entropy weight method and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method is proposed to analyze the level of metro development conditions and coupling coordination conditions of 35 cities in China. According to the calculation results, 35 cities are divided into six categories, and radar charts are constructed to promote the sustainable development of the metro system.

Multi-objective optimization for limiting tunnel-induced damages considering uncertainties

Due to the rapid development of the urban metro system, the situation of new excavation work being conducted adjacent to existing tunnels is quite common and becomes prime hazards in the tunnel design stage, together with uncertainties from the ground condition. To solve this problem, this paper develops a hybrid approach that integrates ensemble learning and non-dominant sorting genetic algorithm-II (NSGA-II) to mitigate the limit support pressure (LSP) and the ground surface deformation (GSD) during the tunnel excavation for improved design. The extreme gradient boosting (XGBoost) algorithm is used to establish ensemble learning models predicting LSP and GSD, where the new tunnel is constructed in parallel to an existing tunnel. NSGA-II is further used to optimize the two targets (i.e., LSP and GSD), considering the uncertainties from geotechnical conditions and errors from the meta-model. With the Monte-Carlo simulation, probability constraints are established to conduct the multi-objective optimization (MOO). Finally, the Pareto front is generated to obtain the best location of the new tunnel, and a comparison is made between MOO with and without considering uncertainties. The best solution is selected by the criterion of the point with the shortest distance from the ideal point. It is found that after considering uncertainties: (1) The improvement percentage of LSP is increased from 9.67% to 11.03%, and that of GSD drops from 2.39% to 0.9%; (2) A higher stability of improvement from optimization is achieved with the standard deviation of improvement percentage drops from 0.310 to 0.298 for LSP and 0.024 to 0.020 for GSD; (3) With a weaker confidence on the meta-model, a higher degree of sacrifice on GSD is observed. The novelty of the proposed approach lies in its capability to not only predict and optimize the damage from excavation adjacent to an existing tunnel, but also consider various types of uncertainties from geological conditions and meta-models to guarantee reliability.

Incorporating travel behavior regularity into passenger flow forecasting

Accurate forecasting of passenger flow (i.e., ridership) is critical to the operation of urban metro systems. Previous studies mainly model passenger flow as time series by aggregating individual trips and then perform forecasting based on the values in the past several steps. However, this approach essentially overlooks the fact that passenger flow consists of trips from each individual traveler. For example, a traveler’s work trip in the morning can help predict his/her home trip in the evening, while this causal structure cannot be explicitly encoded in standard time series models. In this paper, we propose a new forecasting framework for boarding flow by incorporating the generative mechanism into standard time series models and leveraging the strong regularity rooted in travel behavior. In doing so, we introduce returning flow from previous alighting trips as a new covariate, which captures the causal structure and long-range dependencies in passenger flow data based on travel behavior. We develop the return probability parallelogram (RPP) to summarize the causal relationships and estimate the return flow. The proposed framework is evaluated using real-world passenger flow data, and the results confirm that the returning flow—a single covariate—can substantially and consistently improve various forecasting tasks, including one-step ahead forecasting, multi-step ahead forecasting, and forecasting under special events. And the proposed method is more effective for business-type stations with most passengers come and return within the same day. This study can be extended to other modes of transport, and it also sheds new light on general demand time series forecasting problems, in which causal structure and long-range dependencies are generated by the user behavior.

An event-based probabilistic model of disruption risk to urban metro networks

Metro systems serve large populations and form extensive networks. Incidents such as signal failure, train failure, and power failure, pose great challenges to the reliable operation of metro systems around the world. For the Beijing metro system, incidents caused 408 disruptions of train services from 2014 to 2018. These incidents are investigated in detail, and a Monte Carlo approach and incident parameter functions are used to generate stochastically simulated incident events. Based on the simulated incidents, combined passenger flow data and an input-output model, we estimate the risk associated with the Beijing metro system in terms of disrupted passenger flows by considering risk propagation in the network, where both direct passenger loss and indirect passenger flow loss considering interchanges between different lines are considered. Lines at high risk are identified, and a sensitivity analysis is performed to investigate the effects of risk mitigation measures. This study provides a generic risk modeling method for urban metro systems and can improve decision making to manage metro system risk.

A causal inference approach to measure the vulnerability of urban metro systems

Transit operators need vulnerability measures to understand the level of service degradation under disruptions. This paper contributes to the literature with a novel causal inference approach for estimating station-level vulnerability in metro systems. The empirical analysis is based on large-scale data on historical incidents and population-level passenger demand. This analysis thus obviates the need for assumptions made by previous studies on human behaviour and disruption scenarios. We develop four empirical vulnerability metrics based on the causal impact of disruptions on travel demand, average travel speed and passenger flow distribution. Specifically, the proposed metrics based on the irregularity in passenger flow distribution extends the scope of vulnerability measurement to the entire trip distribution, instead of just analysing the disruption impact on the entry or exit demand (that is, moments of the trip distribution). The unbiased estimates of disruption impact are obtained by adopting a propensity score matching method, which adjusts for the confounding biases caused by non-random occurrence of disruptions. An application of the proposed framework to the London Underground indicates that the vulnerability of a metro station depends on the location, topology, and other characteristics. We find that, in 2013, central London stations are more vulnerable in terms of travel demand loss. However, the loss of average travel speed and irregularity in relative passenger flows reveal that passengers from outer London stations suffer from longer individual delays due to lack of alternative routes.

Policy Zoning for Efficient Land Utilization Based on Spatio-Temporal Integration between the Bicycle-Sharing Service and the Metro Transit

Dockless bicycle-sharing is one of the novel transportation modes that emerged in recent years. As a component of the public transit system, it plays an important part in connecting the urban metro system but also generates some problems. With the low parking turnover rate, the oversupply of bicycles will generate parking demand and occupy a large amount of urban public land. To make the dockless bicycle-sharing service a more effective travel mode for connecting the metro system, it is necessary to implement policy zoning with different management strategies for efficient land utilization in the dockless bicycle-sharing system. In this paper, the bicycle-sharing trips data and the metro trips data are utilized to extract indexes to assess the connection performance and operation potential between the bicycle-sharing system and metro transit system. A methodology of coverage area division and policy zoning based on cycling attraction area areas is used. Applying the methodology proposed to a case study in Shanghai, the metro stations in Shanghai are classified into four clusters with different characteristics, including stations with effective land utilization, stations with the potential to promote BS service, stations with deficiencies in BS service, and stations with the potential to improve the land utilization efficiency. According to the different demand patterns and operation performance of the four clusters, corresponding policy suggestions are proposed.

Evaluating the effects of environmental factors and a transfer fare discount policy on the performance of an urban metro system

This study presents a mixed network data envelopment analysis (MNDEA) model with shared resources, a two-stage network, and parallel structures integration developed through the application of directional distance function theory. Environmental factors and a transport policy are introduced into the model to assess the effects on the efficiency and effectiveness of an urban metro system and associated subsystems. Using data from a metro system between 2005 and 2015, a case study is conducted. Following the inclusion of subsystem area population density data and transfer ridership constraints, indications from the MNDEA model suggest that a service technology change could lead to improvements to the metro system and associated subsystems. By contrast, the inclusion of a car ownership variable does not appear to influence the production technology of the metro system and associated subsystems over the same analysis period. The results lend support to the notion that the proposed model could be used to determine the efficiency and effectiveness of urban metro systems and associated subsystems simultaneously. The model also enables the determination of the ratios of shared inputs across production and service processes. The proposed model includes the non-storable services, multi-product, and multi-capacity system features of the metro system. The MNDEA model could enable operators to gain valuable insight into the performance of metro systems and associated subsystems, which in turn could enable the development of improvements. The proposed model could also be applied to analyze the efficiency and effectiveness of the “route-based” or multi-modal aspects of public transport.