Shanghai Metro Network Research Articles

Optimized identification for critical metro stations from urban resilience perspective

Urban rail transit systems and metro-led underground space contribute to urban resilience. The critical level of metro-led underground space in a metro network demands different resilience requirements, thereby rendering the identification of critical metro stations before the evaluation of the resilience contribution essential. This study optimized the identification of critical metro stations in terms of network model construction and evaluation methods to suit urban resilience perspective. The optimized metro network model considered the influence of transfers, difference between above-ground and underground routes, and cost between adjacent stations, to construct an undirected weighted graph of a metro network. Three evaluation metrics: node betweenness, network efficiency loss, and route redundancy, were adopted for a comprehensive identification method of critical stations. The optimized identification method was applied to the Shanghai Metro network (402 stations and 481 links by 2023). The betweenness identified the downtown stations to be more critical, whereas the network efficiency loss and route redundancy identified the critical stations at downtown-suburban connections. Moreover, the vulnerability and distribution of above-ground stations influenced the identification result of critical stations. The identification results are expected to facilitate the resilience evaluation of metro-led underground space, to examine whether the contribution is balanced with the demand.

Impact of extreme rainfall on metro network resilience: Shanghai metro as an example

Extreme rainfall results in urban floods that are known to occur frequently and worldwide. In particular, a metro system faces a high risk of inundation owing to its special geographical location. Consequently, metro network flood resilience has attracted increased attention. This study considered Shanghai as an example to assess the resilience of metro network under extreme rainfall. First, a resilience assessment method of metro networks under extreme rainfall conditions was proposed. This method comprised comprehensive urban waterlogging analysis, construction of a topological structure of metro networks, and resilience assessment of metro networks. Subsequently, the resilience analysis of the Shanghai metro network under extreme rainfall was conducted as a case study based on the above method. The results showed that the disruption of stations and corresponding intervals caused greater damage to network performance. Finally, based on the analysis results, suggestions were provided to improve the flood resilience of the metro network under extreme rainfall conditions.

Optimizing multimodal timetable synchronization of intercity railway and metro for the first service period during holidays

Metro plays a vital role in managing passenger distribution at intercity railway (IR) stations, particularly during holidays when there is a surge in tourist traffic. To efficiently accommodate the high demand for intercity travel, it becomes imperative for metro agencies to optimize holiday timetables. This paper focuses on designing holiday timetables of the first service period for the metro network that connects to an IR station, aiming to enhance multimodal collaboration with IR timetables while ensuring seamless coordination among various metro lines at the network level. A bi-objective model is proposed to maximize the temporal availability of metro network and minimize transfer waiting times for IR passengers traveling in early morning. To solve the model, an improved Artificial Bee Colony algorithm is designed, incorporating adaptive neighbour search and simulated annealing techniques. The effectiveness of the model and algorithm is verified using the Shanghai Metro network and Hongqiao Railway Station. Results indicate a 9.46% increase in the temporal availability of metro network for IR passengers, coupled with a 9.68% reduction in passenger transfer waiting times. Notably, the study reveals that solely advancing operations of the IR-connected metro lines is inefficient. Instead, optimizing train timetables for the entire metro network proves to be a cost-effective approach to enhancing the overall service level of early-morning operations. Furthermore, the study emphasizes the significance of even-numbered train headways in reducing passenger transfer waiting times.

An empirical study on the stochastic long-term travel demands of a large-scale metro network

The widely-existed uncertainty of origin–destination (OD) demand in transportation networks has attracted extensive attention. Most characterizations or modeling of stochastic OD demands in networks assume a homogenous probability distribution, but it lacks empirical studies of large-scale networks to justify this assumption. Given that the long-term continuous automatic fare collection data of metro networks can provide complete OD passenger demand information, this study takes the Shanghai Metro network as an example to empirically examine the stochasticity characteristics of OD passenger demands of metro network. Based on the morning peak OD demand data for 250 weekdays, a local outlier factor method is used to identify and remove outliers in the data. A clustering method is used to cluster the OD pairs, study the fluctuation and distribution characteristics of the OD passenger demands, and select the optimal distribution type through goodness-of-fit indices. The results show that 1) the coefficients of variation of morning peak OD demands in the network are mainly distributed in the range of 0.2–0.6, and different OD pairs have different fluctuations, and the degree of demand fluctuation decreases as the mean increases; 2) the probability distribution types of OD demands based on statistical characteristics are heterogeneous; and 3) the optimal distribution type of OD demands is Poisson, Lognormal/Gamma, and Normal for OD pairs characterized by a small mean and right-skewness, a small mean and skewness close to 0, and a large mean, respectively. In contrast to the simple average-based data processing of OD passenger demand in metro networks, this paper presents a new perspective of mining long-term continuous data to understand the inherent stochasticity of OD passenger demands. The results can provide more realistic and practical inputs and assumptions for theoretical research on stochastic OD demands in metro networks.

Recognizing Critical Stations in Urban Rail Transit Networks Based on the PCA‐TPE Method: Shanghai Metro as an Example

The identification of core stations in urban rail transit (URT) networks remains a vital issue in network structure organization analysis and an integral part of network reliability evaluation. However, the identification of critical stations with a single centrality metric has limitations and the varying interactions between stations cannot be ignored. In this paper, a novel integrated approach is proposed by using the principal component analysis and topological potential considering entropy (PCA‐TPE) method. Taking the Shanghai metro (SHM) network as a case study, a Space L network model is constructed and the network topology characteristics are analyzed. Moreover, the susceptible‐infected (SI) model and the network failure simulation are employed to demonstrate the effectiveness of the proposed method. The results show that the SHM network exhibits characteristics of both small‐world networks and scale‐free networks. According to the experiments of the SI model, the nodes obtained by the PCA‐TPE method have stronger spreading influence than those derived by other methods, especially in the initial stage. The failure simulations illustrate that attacks against the nodes detected by the PCA‐TPE method will lead to devastating network failures. Hence, the proposed method is effective for identifying critical nodes in URT networks, and the findings of the research can provide theoretical evidence for the development planning and emergency management of the public traffic system.

Ground subsidence risk assessment method using PS-InSAR and LightGBM: a case study of Shanghai metro network

ABSTRACT Ground subsidence is a common geological hazard in urban areas that endangers the safety of infrastructure, such as subways. In this study, the ground subsidence risk assessment method considering both ground subsidence intensity and susceptibility is proposed and applied to assess ground subsidence risk of the Shanghai Metro network. Initially, PS-InSAR is used for the ground subsidence survey in the Shanghai Metro area. Subsequently, ten subsidence causal factors are collected, and the LightGBM machine learning algorithm is employed to conduct the ground subsidence susceptibility analysis. Then, a risk matrix is introduced to define ground subsidence risk by combining subsidence intensity and susceptibility. Finally, the risk map is generated in ArcGIS and classified into five levels. The assessment results were used to identify ground subsidence risk at different scales. The results indicate that the risk is higher in the southwest part of the study area, and the ground subsidence risk of the metro network exhibits a regional-related characteristic. On-site investigations were conducted to verify the results. The method enables fast ground subsidence assessment over a large area at a low cost and the assessment results can provide data for the prevention and management of ground subsidence hazards in the city.

Vulnerability assessments of urban rail transit networks based on extended coupled map lattices with evacuation capability

This paper proposes the evacuation capability of station to design the extended coupled map lattices and analyzes the passenger dynamics and station states of urban rail transit networks subjected to the external disturbances. Meanwhile, the commuting ability of station is designed to study the topological vulnerability and the residual flow ratio of passenger flow is involved to study the functional vulnerability of urban rail transit networks. Moreover, three malicious attacks are used to imitate the external disturbances, and Shanghai metro network is taken as the example to illustrate the feasibility and effectiveness of the proposed model. The results show that urban rail transit networks have certain robustness subjected to the external disturbances and the global cascading failures will take place when the external disturbance R≥2.6, which indicates that R=2.6 is the critical threshold of external disturbances to smash urban rail transit networks subjected to three malicious attacks. By comparisons, we can declare that the nodes with the highest betweenness and the largest degree must be provided more protections in daily operation of urban rail transit networks, and the topological structure has a greater impact on network vulnerability compared with transportation function.

Robustness assessments of urban rail transit networks based on user equilibrium with time compensation mechanism

Urban rail transit has become an important mode of urban public transportation and has been paid more attention in the past two decades due to the advantages of low pollution and high efficiency. However, traffic incidents frequently occur in urban rail transit, therefore the robustness must be investigated to improve operation safety. In this paper, the user equilibrium with time compensation mechanism is adopted to study the robustness of urban rail transit networks (URTNs) subjected to failures, and Shanghai metro network is considered as an example to prove the feasibility and effectiveness of the proposed scheme. Meanwhile, the average travel time, the average link-flow and the demand–supply ratio are used to assess the characteristic changes of URTNs subjected to three attack modes. Moreover, the critical nodes and links can be discovered based on the proposed model, and the attacks to these critical nodes and links can greatly decrease the robustness of URTNs. The simulation results show that URTNs have better robustness when suffering the node attack and the link-demand attack and possess worse robustness when suffering the overall-demand attack, and we find that the uneven distribution of passenger flow results in the low network utilization.

Cascading Failure Analysis on Shanghai Metro Networks: An Improved Coupled Map Lattices Model Based on Graph Attention Networks.

Analysis of the robustness and vulnerability of metro networks has great implications for public transport planning and emergency management, particularly considering passengers’ dynamic behaviors. This paper presents an improved coupled map lattices (CMLs) model based on graph attention networks (GAT) to study the cascading failure process of metro networks. The proposed model is applied to the Shanghai metro network using the automated fare collection (AFC) data, and the passengers’ dynamic behaviors are simulated by GAT. The quantitative cascading failure analysis shows that Shanghai metro network is robust to random attacks, but fragile to intentional attacks. Moreover, there is an approximately normal distribution between instant cascading failure speed and time step and the perturbation in a station which leads to steady state is approximately a constant. The result shows that a station surrounded by other densely distributed stations can trigger cascading failure faster and the cascading failure triggered by low-level accidents will spread in a short time and disappear quickly. This study provides an effective reference for dynamic safety evaluation and emergency management in metro networks.

MLCOR Model for Suppressing the Cascade of Edge Failures in Complex Network

As a decisive parameter of network robustness and network economy, the capacity of network edges can directly affect the operation stability and the construction cost of the network. This paper proposes a multilevel load–capacity optimal relationship (MLCOR) model that can substantially improve the network economy on the premise of network safety. The model is verified in artificially created networks including free-scale networks, small-world networks, and in the real network structure of the Shanghai Metro network as well. By numerical simulation, it is revealed that under the premise of ensuring the stability of the network from the destruction caused by initial internal or external damage on edge, the MLCOR model can effectively reduce the cost of the entire network compared to the other two linear load–capacity models regardless of what extent of the destruction that the network edges suffer initially. It is also proved that there exists an optimal tunable parameter and the corresponding optimal network cost for any BA and NW network topology, which can provide the reference for setting reasonable capacities for network edges in a real network at the stage of network planning and construction, promoting security and stability of network operation.

A Data-Efficient Approach for Evacuation Demand Generation and Dissipation Prediction in Urban Rail Transit System

Predicting evacuation demand, including its generation and dissipation process, for urban rail transit systems under disruptions, such as line and station closure, often requires comprehensive historical data recorded under homogeneous situations. However, data under disruptions are hard to collect due to various reasons, which makes traditional methods impractical in evacuation demand prediction. To address this problem from the modeling perspective, we develop a data-efficient approach to predict evacuation demand for urban rail transit systems under disruptions. Our model-based approach mainly uses historical data obtained from the natural state, when no shocks take place. We first formulate the mathematical representation of the evacuation demand for every type of urban rail transit station. Input variables in this step are location features related to the station under the disruption, as well as an origin–destination matrix under the natural state. Then, based on these mathematical expressions, we develop a simulation system to imitate the spatio-temporal evolution of evacuation demand within the whole network under disruptions. The transport capacity drop under disruptions is used to describe the disruption situation. Several typical scenarios from the Shanghai metro network are used as examples to implement the proposed method. The results show that our method is able to predict the generation and dissipation processes of evacuation demand, as well model how severely stations will be affected by given disruptions. One general observation we draw from the results is that the most vulnerable stations under disruption, where the locations peak evacuation demand occurs, are mainly turn-back stations, closed stations, and the transfer stations near closed stations. This paper provides new insight into evacuation demand prediction under disruptions. It could be used by transport authorities to better respond to the urban rail transit system disruption.

Measure Vulnerability of Metro Network Under Cascading Failure

Metro network is the important lifeline in modern cities. Its stability and reliability are critical for guaranteeing the urban residents' efficient commuting and continuous operation of urban functions. A series of disruptions show the vulnerability of metro network, and even the breakdown of a single node is sufficient to collapse the entire network due to cascading failure. However, the vulnerability assessments of metro network in the previous studies neglect the impact of cascading failure on the drop of network service performance. This paper measures the vulnerability of metro network by capturing the demand loss and travel delay under cascading failure. First, a load-capacity based model is developed to describe the dynamic process of cascading failures. Then, a weighted composite index composed of demand loss and travel time delay under cascading failure is used to measure metro network vulnerability. Finally, taking Shanghai metro network as an example, five attack scenarios are simulated to investigate cascading failure process and network vulnerability. The results reveal that cascading failures result in severe consequences in the metro network. The vulnerability of metro network without considering cascading failure is underestimated. The decrease in the tolerance parameter leads to the increase in metro network vulnerability. Random attack on one node is the most sensitive to the tolerance parameter. Node betweenness, cascading failure path and the initial load on the overloaded nodes also affect metro network vulnerability. This study provides a new perspective for understanding vulnerability of metro network, and also provides insights for improving operation reliability and stability of the network, as well as for designing emergency strategies to protect the network.

Shanghai Metro Constructs an “Intelligent Metro”

The World Artificial Intelligence (AI) Conference 2020 opened in Shanghai on July 9, 2020. The AI application scenarios presented by Shanghai Shentong Metro Group Co., Ltd. (Shentong Metro Group) have attracted a lot of attention of visitors. On July 9, 2020, the total passenger flow of Shanghai Metro exceeded 10 million, reaching 10.048 million, and Shanghai will enter the normal state of “ten million passenger flow” every day. At present, the operating mileage of Shanghai Metro reaches 705km, ranking first in the world. According to the new line planning and construction trend, the Shanghai Metro network will continue to expand, and there will be even more passenger flows in the future.

Route redundancy-based approach to identify the critical stations in metro networks: A mean-excess probability measure

Identifying the critical elements of metro networks attracts a growing attention due to the significant impact of accidents on metro systems. The existing measures can be divided into two types: the localized measures (e.g., flow, centrality, etc.) in a normal network state and the impact-based measures by assuming failure scenarios to conduct before-and-after analysis. In this paper, we develop a new method to identify the critical stations in metro systems based on the concept of route redundancy. Different from the localized measures, route redundancy describes the origin-destination effective connections under potential disruptions explicitly from travelers’ perspective. Compared with the impact-based measures, the proposed method does not need to enumerate disruption scenarios and to reevaluate the resultant network performances. Specifically, the mean-excess criticality probability is proposed as a risk measure to calculate the criticality of each station in a metro network. A realistic case study based on the Shanghai metro network is conducted to demonstrate the features of the proposed method. The results indicate that the critical stations are not necessarily transfer stations or those with a high degree, and the important stations based on betweenness, passenger flow and network efficiency are not necessarily critical for the network redundancy. The proposed method could assist in a cost-effective resource allocation and an informed decision-making for strategically enhancing the metro network resiliency.

Service-Oriented Load Balancing Approach to Alleviating Peak-Hour Congestion in a Metro Network Based on Multi-Path Accessibility

To further improve the service quality and reduce safety risks in current congested metro systems during peak hours, this paper presents a load balancing (LB) approach so that available capacity can be utilized more effectively in order to alleviate peak hour congestion. A set of under-utilized yet effective alternative routes were searched using a deletion algorithm (DA) in order to share the passenger loads on overcrowded metro line segments. An optimization model was constructed based on an improved route generalized time utility function considering the penalties of both in-vehicle congestion and transfers. A detailed load balancing solution was generated based on the proposed algorithm. A real-world example of three overloaded metro line segments in the Shanghai metro network were selected and used to verify the feasibility and validity of the developed load balancing method. The results show that the load balancing method can effectively reduce the overcrowding situation to a great extent. Finally, two prospective inducing schemes are discussed to help implement the load balancing solution in the actual metro system in an efficient and effective manner.

Modeling network resilience of rail transit under operational incidents

In the context of urban rail network analysis, most studies have focused on vulnerability analysis developing methodologies to measure consequences on network performance under destroy events. Few works are reported addressing the modeling of rail transit network resilience under operational incidents which could be more important analyzing the network performance changes under daily operations. This study demonstrates a resilience approach for rail transit network under daily operational incidents. Integrating the network topological and passenger volume characteristics, this approach explicitly accounts for the impacts of accumulative affected passengers quantifying the varying resilience of rail network with time under different incidents. The proposed methodology was applied to Shanghai Metro Network in Shanghai, China. Results show that critical stations are identified differently depending on duration time of different incidents and characteristics of the failed stations, and stations on network legs could be more important than those with redundant rail alternatives. Conclusions of this research would also have practical implications for the management and decision making of rail transit network under daily operational incidents.

Resiliency assessment of urban rail transit networks: Shanghai metro as an example

This paper presents a general framework to assess the resilience of large and complex metro networks by quantitatively analyzing its vulnerability and recovery rapidity within unifying metrics and models. The connectivity performance of network is indicated by the network efficiency. The resilience of a metro network can be associated to the network performance loss triangle over the relevant timeline from the occurrence of a random or intentional disruption to full recovery. The proposed resilience model is applied to the Shanghai metro network with its 303 stations and 350 links as an example. The quantitative vulnerability analysis shows that the Shanghai metro with its L-space type of topology has a strong robustness regarding connectivity under random disruption but severe vulnerability under intentional disruption. This result is typical for small-world and scale-free networks such as the Shanghai metro system, as can be shown by a basic topological analysis. Considering the case of one disrupted metro station, both the vulnerability and resilience of the network depend not only on the node degree of the disrupted station but also on its contribution to connectivity of the whole network. Analyzing the performance loss triangle and the associated cost from loss of operational income and repair measures, an appropriate recovery strategy in terms of the optimum recovery sequence of stations and the optimum duration can be identified in a structured manner, which is informative and helpful to decision makers.

A station-based rail transit network vulnerability measure considering land use dependency

Natural disasters, intentional attacks, and operational incidents are posing increasing threats on rail transit network. The vulnerability of rail transit network becomes an important concern of researchers and rail managers. This paper proposes a station-based accessibility approach addressing passenger flow and land use characteristics in rail transit network vulnerability analysis. Land use variables are measured as the independency degree on rail transit. The reduction ratio of network accessibility before and after incidents is calculated to measure the potential consequences. Based on results of comparisons with existing methods with the help of an example problem, the proposed accessibility measure demonstrates better and more reasonable results as not only the rail network and passenger flow but also the land use and travel alternative variables interacting with rail transit are accounted. The proposed method is then applied to Shanghai metro network. The data for analysis include rail transit network data, passenger flow data, and land use data around stations. Results indicate that the vulnerability of rail transit network is jointly affected by its network topology, passenger flow, and land use variables. Unbalanced land use, high plot ratio, and the less travel alternatives will increase the dependency of land on rail transit travel, leading to high network vulnerability once disrupted. Results of this work will inform rail transit managers of the degree of network vulnerability and critical stations and links as well as the land use dependency on vulnerability. Findings of this study may have implications not only for the planning of other transit modes to enhance the resilience of public transit network in vulnerable areas but also for the land use development around rail stations.

Vulnerability analysis of urban rail transit based on complex network theory: a case study of Shanghai Metro

With increasing passenger flows and construction scale, metro systems in metropolises have entered a new era of networking operation and become the most effective way to alleviate and decrease traffic congestion. However, frequent occurrence of random failures and malicious attacks pose a serious threat to metro security and reliability. Thus, it is necessary to quantitatively evaluate the vulnerability of the metro network to different failures or attacks from a networking perspective. Based on the complex network theory, this study took the Shanghai Metro Network (SMN) as an example to investigate vulnerability of a weighted metro network in responding to random failures as well as malicious attacks. In particular, compared to topological networks, the vulnerability of weighted networks was analyzed to investigate how traffic and spatial constraints influence the transport system’s vulnerability, since topological features of complex networks are often associated with the weights of the edges and spatial constraints. Simulation results show that the SMN is robust against random failures but fragile for malicious attacks. The vulnerability analysis of weighted properties shows that all targeted attacks are capable to shatter the network’s communication or transport properties at a very low level of removed nodes and the highest betweenness attack strategy is the most effective mode to cause destructive effects on SMN among five attack or failure strategies. The inclusion of passenger flows provides evidence for the view that topological networks cannot convey all the information of a real-world network and traffic flow in the network should be considered as one of the key features in the finding and development of defensive strategies. Our results provide a richer view on complex weighted networks in real-world and possibilities of risk analysis and policy decisions for the metro operation department.

Measuring vulnerability of urban metro network from line operation perspective

Urban metro systems are subject to recurring service disruption for various reasons, such as mechanical or electrical failure, adverse weather, or other accidents. In recent years, studies on metro networks have attracted increasing attention because the consequence of operational accidents is barely affordable. This study proposes to measure the metro network vulnerability from the perspective of line operation by taking the Shanghai metro network as a case study. As opposed to previous studies that focused largely on disruption of important nodes or links, this study investigates the disruption from the line operation perspective. Betweenness centrality (BC) and passenger betweenness centrality (PBC), number of missed trips, weighted average path length, and weighted global efficiency were analyzed considering relative disruption probability of each line. Passenger flow distribution and re-distribution were simulated for different disruption scenarios based on all-or-nothing assignment rule. The results indicate that the metro lines carrying a large number of passengers generally have a significant impact on the network vulnerability. The lines with circular topological form also have a significant influence on passenger flow re-distribution in case of a disruption. The results of this study provide suggestions on metro system administration for potential improvement of the performance of operation, and passengers may meanwhile have an improved alternate plan for their commute trip when a disruption occurs.