Shanghai Metro Research Articles

Cascading Failure and Resilience of Urban Rail Transit Stations under Flood Conditions: A Case Study of Shanghai Metro

Cascading Failure and Resilience of Urban Rail Transit Stations under Flood Conditions: A Case Study of Shanghai Metro

Identification of Moving Train Axle Loads for Simply Supported Composite Beam Bridges in Urban Rail Transit

With the rapid urbanization and expansion of rail transit systems, the axle loads of trains, which are a critical aspect of their configuration, have significantly increased. This increase poses substantial potential threats to the safety and service life of existing bridges within urban rail transit networks. Therefore, it is imperative to develop methods for monitoring and identifying train axle loads. In this study, a strain field measurement scheme was devised and implemented for an operational simply supported composite beam bridge in urban rail transit. This involved numerical modeling and validation of the bridge’s structural response, followed by the calculation of strain influence lines at specific measurement points. Subsequently, a method for identifying train axle loads, considering the dynamic amplification effect, was established. This method integrates principles from strain influence lines with mathematical optimization techniques. Specifically, the axle loads of locomotives on the forward AC03 type trains of Shanghai Metro Line 3 were found to conform to a logarithmic normal distribution model, while those of middle carriages followed a normal distribution model. Their respective mean axle loads were determined as 9.64 t and 10.77 t, with a shared variance of 0.8. Similarly, the axle loads of locomotives and middle carriages on reverse AC03-type trains also followed normal distribution models, with identical mean values around 10.5 t. The variances for axle loads of locomotives and middle carriages of reverse trains were found to be 1.36 and 0.8, respectively. The developed method effectively enables the monitoring of train axle loads and assessment of their impact on bridge structures, therefore enhancing safety and operational reliability within urban rail transit systems.

Temporal-spatial-fusion-based risk assessment on the adjacent building during deep excavation

Foundation pit excavation will inevitably cause uneven ground settlement to pose potential risks to adjacent structures and infrastructures. To better perceive the risk status of adjacent buildings using multi-source information, a temporal-spatial-fusion-based risk assessment (TSFRA) model under the consideration of uncertainty and causality is developed by integrating FAHP (Fuzzy Analytic Hierarchy Process), DGDT (DAG-GNN, DEMATEL method, and topological analysis), and CM (cloud model). More specifically, FAHP handles temporal weights of excavation conditions based on expert knowledge. DGDT determines spatial weights for monitoring points, with DAG-GNN constructing causal graphs in a data-driven manner, DEMATEL handling global interactions, and topological analysis calculating node importance. CM can finally fuse the temporal (excavation conditions) and spatial (monitoring points) weights with settlement monitoring data, resulting in qualitative assessments of the overall risk status of adjacent buildings in uncertain environments. The proposed TSFRA is verified in the case of a Shanghai metro station construction project. Results indicate that the perceived risk of the project is at a relatively low level, consistent with the actual condition. High-risk excavation conditions and locations can be easily identified. There is a trend toward higher risk during the excavation of soft soil layers, and thus some risk control measures can be formulated to avoid potential risk events. In short, the weight determination and fusion method in TSFRA contribute to handling uncertainties in expert judgments and causalities in collected data, which can practically provide more reliable decision-making in excavation-induced risk assessment and control for ensuring the safety of adjacent buildings.

Strain characteristics of reinforced soft clay around tunnel under metro loads

This study investigates the Strain characteristics of reinforced soft clay surrounding a tunnel subjected to cyclic loading, employing the Global Digital Systems cyclic triaxial test on the saturated reinforced clay near Shanghai Metro Line 4’s Hailun Road station. Initially, the study examines the impacts of frequency, dynamic stress amplitude (DSA), and loading cycles on cumulative plastic strain. Following this, the orthogonal design method was employed to organize tests, and an evaluation method for assessing soil strain was developed through mathematical statistical analysis. The results indicate at the constant vibration frequency, cumulative plastic strain increases with an increase in DSA and decreases with as load frequency increases. DSA is the primary factor influencing the axial deformation of subway tunnels, whereas the interaction between load frequency and vibration frequency is negligible. The findings suggest that measures to prevent and control subway tunnel settlement should concentrate on DSA during the early stages of subway operation.

Assessing metro network vulnerability with turn-back operations: A Monte Carlo method

Metro network disruptions caused by accidents or maintenance can significantly impact its serviceability. Traditionally, graph theory and complex network methods are utilized to examine topological properties by removing nodes, links, or entire lines. However, in practice, it is the turn-back operation that determines the service cancellations and the affected area. This study assesses metro network vulnerability by considering the turn-back section as the Vulnerability Analysis Unit (VAU). Indicators for evaluating topological accessibility and operational robustness are proposed to assess network performance under disruptions. Monte Carlo method is applied to generate scenarios, involving the removal of various combinations of VAUs. A case study is conducted on Shanghai Metro system. The results indicate that on average, when one VAU is damaged, the travel time will increase by 2.39 minutes and 1.75 % of passengers will move to other transport modes. The critical vulnerable sections are mainly located in radial lines connecting to the loop lines, while those located in or inside the loop line exhibit higher robustness.

Real-time train timetabling with virtual coupling operations on a Y-type metro line

The spatiotemporal imbalance of passenger flows is a prominent characteristic of urban rail transit systems. To match the provided transportation capacity with passenger distribution, this study considers an integer linear programming model to optimize train operation on a Y-type line, including the train timetable, rolling stock circulation plan and virtual coupling/uncoupling strategy that enables trains to switch between different compositions or configurations during operations. To enhance solution efficiency, certain integer decision variables in the model are relaxed to be continuous, and it is proved that this does not affect the optimal value of the model. To account for the dynamic nature of passenger flows, a “prediction + optimization” method with the rolling optimization framework, which utilizes real-time predicted passenger flow data to enable train operations to be performed and adjusted in response, is proposed. Three variants of the proposed model are embedded to meet the real-time requirements of operations. Numerical experiments verify the effectiveness and applicability of our proposed approach, with real-world data from Shanghai Metro Line 5. The computational results demonstrate that our method performs well under operation scenarios with both normal and abnormal passenger flows. Compared to fixed train composition, virtual coupling can perform much better in both peak and off-peak periods.

Evaluating rail transit assignment models in the temporal dimension: The problem and its solution

Passenger flow is the foundation for urban rail transit (URT) operations. However, its calculated results from assignment models may deviate from the actual situation in both spatial and temporal dimensions, which arouses more attention and needs to be evaluated in particular. On the other hand, onboard video data from URT trains provides a potential way for model evaluation. This study defines the evaluation problem and proposes a methodological solution for evaluating rail transit assignment models in the temporal dimension, including qualitative validation and difference quantification. A suitable time granularity is determined for the best effectiveness and onboard video data is used for actual passenger flow extraction. The gap between the actual and the calculated by the model is identified with nonparametric statistical techniques (NPSTs) and quantified with time series similarity measurement (TSSM) methods. A case study on the Shanghai metro demonstrates the performance of the proposed approach, and several practice implications for URT operation agencies are also discussed.

Subway track foundation settlement deformation prediction based on the BiLSTM-AdaBoost model

The rapid economic expansion has spurred extensive construction near subway networks, impacting the stability of their track foundations. Consequently, it’s crucial to monitor and predict settlement in subway track foundations. However, the dynamic deformation patterns often exhibit nonlinearity and non-stationarity, posing challenges for traditional linear regression models. To tackle this, our study integrates the BiLSTM (bi-directional long short-term memory) network with the AdaBoost ensemble learning algorithm. Using settlement data from Shanghai metro monitoring points, the model is trained and evaluated employing R2 (coefficient of determination), MAE (mean absolute error), and RMSE (root mean square error). Results show that our proposed model displays superior predictive accuracy compared to the LSTM and the BiLSTM, with an average training set R2 of 0.99, test set R2 of 0.78, average MAE of 0.32 mm, and average RMSE of 0.4 mm. Consequently, for forecasting subway track foundation deformations, employing our network model ensures highly accurate predictive capabilities.

Numerical Simulation Study Considering Discontinuous Longitudinal Joints in Soft Soil under Symmetric Loading

In shield tunneling, the joint is one of the most vulnerable parts of the segmental lining. Opening of the joint reduces the overall stiffness of the ring, leading to structural damage and issues such as water leakage. Currently, the Winkler method is commonly used to calculate structural deformation, simplifying the interaction between segments and soil as radial and tangential Winkler springs. However, when introducing connection springs or reduction factors to simulate the joint stiffness of segments, the challenge lies in determining the reduction coefficient and the stiffness of the springs. Currently, the hyperstatic reflection method cannot simulate the discontinuity effect at the connection of the tunnel segments, while the state space method overlooks the nonlinear interaction between the tunnel and the soil. Therefore, this paper proposes a numerical simulation method considering the interaction between the tunnel and the soil, which is subjected to compression rather than tension, and the discontinuity of the joints between the segments. The model structure and external load are symmetrical, resulting in symmetrical calculation results. This method is based on the soft soil layers and shield tunnel structures of the Shanghai Metro, and the applicability of the model is verified through deformation calculations using three-dimensional laser scanning point clouds of sections from the Shanghai Metro Line 5. When the subgrade reaction coefficient is 5000 kN/m3, the model can effectively simulate the deformation of operational tunnels. By adjusting the bending stiffness of individual connection springs, we investigate the influence of bending stiffness reduction on the bending moment, radial displacement, and rotational displacement of the ring. The results indicate that a decrease in joint bending stiffness significantly affects the mechanical response of the ring, and the extent and degree of this influence are correlated with the joint position and the magnitude of joint bending stiffness.

Transport capacity analysis for sharing-corridor metro lines under virtual coupling

In this study, a method is proposed to analyse the capacity improvement ability of using virtual coupling technology to the sharing-corridor metro lines for solving the problem of capacity bottleneck. A mixed integer linear programming model is developed to optimise the train timetable of sharing-corridor lines under the assumption of using the virtual coupling technology, considering the constraints related to train timetabling, virtual coupling technology, and passenger assignment, with the objective to improve the service quality. The model adopts the departure times, arrival times, and whether each train service is planned as virtually coupled, with its adjacent front one or the following one, as decision variables. Furthermore, a method is proposed to evaluate the capacity utilisation referring to UIC 406 and considering the feature of virtual coupling technology. The experiments on Shanghai Metro Line 3 and Line 4 are implemented to verify the effectiveness on transport capacity and passenger service quality of the proposed method. The results show that using virtual coupling technology can launch eight more train services during morning peak hours and at most reduce passengers’ waiting time by 73.8 s.

Digital twin-based quantification of carbon emission from foundation pit construction: A case study of a station in Shanghai Metro

This study focused on achieving the carbon peaking and carbon neutrality goal. A carbon emission calculation method based on the entire process of excavation construction and the emission factor method for the foundation pit construction of a metro station was proposed. This method facilitated quantitative calculation of total carbon emissions in metro foundation pit engineering, which was then incorporated into a self-designed digital twin platform to drive sustainability efforts. The proposed calculation approach was verified in the foundation pit of Huangpi South Road Station (Shanghai Metro Line No. 14). (1) The pit construction process of the targeted metro station emitted 127,439t of carbon dioxide equivalent. (2) From the material perspective, reinforcing steel and concrete accounted for a significant proportion of the overall carbon emissions. Whereas, from the construction perspective, the engineering process of supporting structures accounted for a considerable proportion of the carbon emissions. (3) A comparative analysis of different construction zones revealed that the carbon emissions during the construction stage of the metro pit project were closely related to the amount of earthwork and size of the deep excavation engineering. Practically, the discovered knowledge of carbon emission characteristics can be visualized in a digital twin.

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.

Field-monitoring and numerical investigation of a super-large-diameter shield tunnel undercrossing existing metro tunnels

The Beiheng Expressway in Shanghai was built using a super-large shield with a diameter of 15 m, and its construction impact on adjacent underground structures cannot be overlooked. This newly built tunnel undercrossed Shanghai Metro Line 18 with a minimum distance of 7.86 m. Noticeable additional movements induced by the crossing can be observed in both lines according to in-situ data, initially manifested as slight settlement followed by atypically pronounced heave. The maximum uplifts of the up and down lines reach 7.23 and 5.09 mm, respectively. Subsequently, a numerical study was conducted on this crossing case, and its effectiveness was verified using in-situ data. The results revealed- the soil-structure interactions during the crossing period. The displacement curves in the longitudinal direction of the metro tunnels exhibited asymmetry owing to the oblique crossing, with greater deformation occurring in the region that was crossed earlier than in the region crossed later. Significant heave was also observed for the newly built segmental linings and surrounding ground, indicating pronounced floating induced by large-diameter excavation, which in turn caused the existing metro lines to move upward simultaneously.

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.

New indices of the recovery effect of the large deformation shield tunnel based on a light-disturbance grouting model test

The sudden surface surcharge is a key disturbance that causes excess transverse deformation of the existing shield tunnel underneath. The light-disturbance grouting technique, which is applied on two sides of a tunnel spring-line, is an effective way to control or even recover the large tunnel structural deformation. Based on the 1g model test of grouting for the shield tunnel in the sand layer using the Shanghai Metro shield tunnel as a prototype, a numerical model is established based on the FLAC3D software. Meanwhile, the numerical simulation method of simulating grouting involves the application of the virtual expansion stress. The grouting transfer efficiency and recovery bias are proposed in this paper and used as the evaluation indexes of the recovery effect of light-disturbance grouting on the shield tunnel. The findings indicate that irrespective of various overloading modes and symmetrical single-row grouting, the transfer efficiency remains approximately 12%, which may potentially serve as a optimization index for determining the number of grouting rows. Moreover, the observed disparity in recovery bias within tunnels experiencing asymmetric deformation underscores the significance of joint behavior, emphasizing the necessity for closer monitoring during grouting procedures.

Study on dynamic response and long-term settlement of silty soil around Shanghai metro tunnel

At present, the method for calculating long-term tunnel settlement predictions under metro loading considers only one working condition of passenger loading, which is inconsistent with actual working conditions. To establish a tunnel settlement model that accounts for variations in passenger flow, this study uses data mining methods to categorize metro operation into three working conditions: "peak period, secondary period, and low period." The impact of these passenger flow conditions on the dynamic response of the soil around the tunnel is analyzed. Then, based on the principles of calculus, a calculus-based prediction model is established to consider the changing patterns of metro passenger flow. The model is applied to analyze the long-term settlement characteristics of Shanghai Metro Line 10. The results indicate that, under identical conditions, soil displacement and dynamic deviatoric stress around the tunnel increase with passenger capacity. The calculus prediction model aligns more closely with actual working conditions than the conventional model. The predicted tunnel settlement of Shanghai Metro Line 10 after 20 years of operation is approximately 37.07 mm, with most settlement occurring in the early stages, primarily due to cumulative plastic deformation of the soil.

Parallel framework of a multi-graph convolutional network and gated recurrent unit for spatial–temporal metro passenger flow prediction

Metro passenger flow prediction is a critical problem in metro transport systems. However, recent studies have either overlooked spatial information on the metro network or primarily focused on modeling spatial dependencies using only the physical topology. To achieve accurate metro passenger flow (inflow and outflow at each station of a network) prediction, this study proposes a joint prediction model that combines the multi-graph convolution network and the gated recurrent unit (GRU). In addition to exploring location topology relationships, the proposed model selects two non-Euclidean spatial dependencies in metro passenger flow prediction to design essential graph elements as part of the stacked spatial block. Three spatial relationships (adjacency, similarity, and correlation) are integrated in parallel with the GRU network. The metro passenger flow prediction framework ASC-GRU (adjacency, similarity, correlation, and gated recurrent unit) is designed to mitigate the distortion of results during the capturing of passenger flow spatial–temporal features. Finally, ASC-GRU is tested using two datasets from the Hangzhou and Shanghai metro networks in China, and the error metrics of different models are compared and analyzed to verify the effectiveness and feasibility of ASC-GRU. The test results demonstrate that the proposed model outperforms other baseline models in passenger flow prediction over long time intervals and large networks. In particular, compared with the best performance of the baselines, the average reduction is around 3%, 12% and 13% in metrics of MAPE, MAE and RMSE, respectively.

Correlation-based feature selection and parallel spatiotemporal networks for efficient passenger flow forecasting in metro systems

This paper presents a novel framework for predicting metro passenger flow that is both interpretable and computationally efficient. The proposed method first uses a correlation-based spatiotemporal feature selection strategy (Cor-STFS) to identify the optimal input scheme for the prediction model, effectively reducing unnecessary interference. The framework then introduces a new multivariate passenger flow prediction architecture called STA-PTCN-BiGRU, which combines a spatiotemporal attention (STA) mechanism, parallel temporal convolutional networks (PTCN), and bidirectional gated recurrent units (BiGRU) to capture the dynamic internal patterns of passenger flow. By utilising parallel computing, this architecture significantly reduces resource consumption. The effectiveness of the proposed approach is evaluated using four datasets from the Shanghai Metro. Experimental results show that the new method outperforms baseline approaches in terms of root mean square error (RMSE), mean absolute error (MAE), and symmetric mean absolute percentage error (SMAPE), achieving average reductions of 9.98%, 8.08%, and 13.29% in these metrics, respectively.

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.

ADF-Net: An Attention-Guided Dual-Branch Fusion Network for Building Change Detection near the Shanghai Metro Line Using Sequences of TerraSAR-X Images

Building change detection (BCD) plays a vital role in city planning and development, ensuring the timely detection of urban changes near metro lines. Synthetic Aperture Radar (SAR) has the advantage of providing continuous image time series with all-weather and all-time capabilities for earth observation compared with optical remote sensors. Deep learning algorithms have extensively been applied for BCD to realize the automatic detection of building changes. However, existing deep learning-based BCD methods with SAR images suffer limited accuracy due to the speckle noise effect and insufficient feature extraction. In this paper, an attention-guided dual-branch fusion network (ADF-Net) is proposed for urban BCD to address this limitation. Specifically, high-resolution SAR images collected by TerraSAR-X have been utilized to detect building changes near metro line 8 in Shanghai with the ADF-Net model. In particular, a dual-branch structure is employed in ADF-Net to extract heterogeneous features from radiometrically calibrated TerraSAR-X images and log ratio images (i.e., difference images (DIs) in dB scale). In addition, the attention-guided cross-layer addition (ACLA) blocks are used to precisely locate the features of changed areas with the transformer-based attention mechanism, and the global attention mechanism with the residual unit (GAM-RU) blocks is introduced to enhance the representation learning capabilities and solve the problems of gradient fading. The effectiveness of ADF-Net is verified using evaluation metrics. The results demonstrate that ADF-Net generates better building change maps than other methods, including U-Net, FC-EF, SNUNet-CD, A2Net, DMINet, USFFCNet, EATDer, and DRPNet. As a result, some building area changes near metro line 8 in Shanghai have been accurately detected by ADF-Net. Furthermore, the prediction results are consistent with the changes derived from high-resolution optical remote sensing images.