Beijing Subway Research Articles

Diversity and Influencing Factors of Public Service Facilities in Urban (Suburban) Railway Life Circle—Evidence from Beijing Subway Line S1, China

Diversity and Influencing Factors of Public Service Facilities in Urban (Suburban) Railway Life Circle—Evidence from Beijing Subway Line S1, China

Field Study and Analysis of Passenger Density in the Beijing Subway Transfer Hall

Passenger density is a major factor influencing indoor climate conditions and the corresponding energy consumption in the transfer halls of metro transportation hubs. In this study, passenger flow in a subway transfer hall in Beijing was measured across three typical seasons. A differential equation model was established to account for the characteristics of passenger density. Based on this, a passenger density prediction model was developed and utilized for analysis. The results show that passenger density is primarily affected by operation time and the surrounding population composition, with a relatively weak correlation to outdoor weather conditions. Additionally, passenger density differs significantly between weekdays and weekends, with the weekday peak being 2.5 times higher than that on weekends. During the four peak hours on weekdays, both in the morning and evening, passenger density exceeds the design capacity, resulting in insufficient fresh air supply. However, for most of the weekday hours and for all hours on weekends, passenger density remains below the design capacity. This indicates a substantial potential for energy savings by adjusting the fresh air supply according to actual passenger density.

Estimation of schedule preference and crowding perception in urban rail corridor commuting: An inverse optimization method

This paper introduces an inverse optimization method to uncover commuters’ schedule preference and crowding perception based on aggregated observations from smart card data for an urban rail corridor system. The assessment of time-of-use preferences typically involves the use of econometric models of discrete choice based on detailed travel survey data. However, discrete choice models often struggle with potential endogeneity issues in behavioral observations when estimating individual samples from massive transit data with limited exogenous identifying information. This motivates us to employ an equilibrium modeling approach to capture the dynamism hidden in commuters’ departure time decision-making from aggregations. Assuming user optimality in observed choices, an inverse optimization method is proposed to find a set of preference parameters in the stochastic user equilibrium-based morning commuting model with heterogeneous commuters so that the resulting equilibrium pattern best approximates the observed departure rate distribution over time. The proposed inverse optimization problem can be formulated by a bi-level programming model and a sensitivity analysis-based solution framework is further designed for model estimation. Lastly, the smart card data and train timetable data from the rail corridor along the Beijing Subway Batong Line are synthesized for a case study to estimate commuters’ departure time choice preferences during morning peak periods, as well as to validate the robustness and practicality of the proposed method.

Wasserstein distributionally robust optimization for train operation and freight assignment in a metro-based underground logistics system

As a shared passenger-and-freight transportation mode, the metro-based underground logistics system (M-ULS) can significantly alleviate negative effects caused by ground transportation vehicles and improve urban sustainability. Against this background, this paper addresses dedicated freight train operation and freight assignment problems in the M-ULS without affecting passenger service during the off-peak period. More importantly, this paper proposes a flexible skip-stop strategy to reduce the increased operational cost of freight trains. To hedge against uncertainty, this paper formulates a distributionally robust chance-constrained programming model by limiting uncertain freight demands to a Wasserstein ambiguity set. Moreover, the distributionally robust optimization model for a Wasserstein ball with the 1-norm can be transformed into a mixed-integer linear program, which allows for an efficient solution via CPLEX, thereby enabling metro operators to use it. To implement this study in real-life applications, a case study is conducted based on the Beijing Subway Yizhuang Line. The computational results verify the superiority of the skip-stop strategy and distributionally robust optimization approach proposed in this study. Finally, some managerial suggestions are provided for the metro operators.

Train Distance Estimation for Virtual Coupling Based on Monocular Vision.

By precisely controlling the distance between two train sets, virtual coupling (VC) enables flexible coupling and decoupling in urban rail transit. However, relying on train-to-train communication for obtaining the train distance can pose a safety risk in case of communication malfunctions. In this paper, a distance-estimation framework based on monocular vision is proposed. First, key structure features of the target train are extracted by an object-detection neural network, whose strategies include an additional detection head in the feature pyramid, labeling of object neighbor areas, and semantic filtering, which are utilized to improve the detection performance for small objects. Then, an optimization process based on multiple key structure features is implemented to estimate the distance between the two train sets in VC. For the validation and evaluation of the proposed framework, experiments were implemented on Beijing Subway Line 11. The results show that for train sets with distances between 20 m and 100 m, the proposed framework can achieve a distance estimation with an absolute error that is lower than 1 m and a relative error that is lower than 1.5%, which can be a reliable backup for communication-based VC operations.

Prediction of Inbound and Outbound Passenger Flow in Urban Rail Transit Based on Spatio-Temporal Attention Residual Network

Passenger flow prediction is a critical approach to ensure the effective functioning of urban rail transit. However, there are few studies that combine multiple influencing factors for short-term passenger flow prediction. It is also a challenge to accurately predict passenger flow at all stations in the line at the same time. To overcome the above limitations, a deep learning-based method named ST-RANet is proposed, which consists of three spatio-temporal modules and one external module. The model is capable of predicting inbound and outbound passenger flow for all stations within the network simultaneously. We model the spatio-temporal data in terms of three temporal characteristics, including closeness, period, and trend. For each characteristic, we construct a spatio-temporal module that innovatively integrates the attention mechanisms into the middle of residual units and convolutional neural networks (CNNs) to extract and learn spatio-temporal features. Subsequently, the results of the three modules are integrated using a parameter matrix method, which allows for dynamic aggregation based on data. The integration results are further combined with external factors, such as holidays and meteorological information, to obtain passenger flow prediction values for each station. The proposed model is validated using real data from Beijing Subway, and optimized parameters are applied for 30-min granularity passenger flow predictions. Comparing the performance against 5 baseline models and verifying with data from multiple lines, the results indicate that the proposed ST-RANet model shows the best results. It is demonstrated that the method proposed in this paper has high prediction accuracy and good applicability.

Metro Track Geometry Defect Identification Model Based on Car-Body Vibration Data and Differentiable Architecture Search

Efficient and low-cost modes for detecting metro track geometry defects (TGDs) are essential for condition-prediction-based preventive maintenance, which can help improve the safety of metro operations and reduce the maintenance cost of metro tracks. Compared with the traditional TGD detection method that utilizes the track geometry car, the method that uses a portable detector to acquire the car-body vibration data (CVD) can be used on an ordinary in-service train without occupying the metro schedule line, thereby improving efficiency and reducing the cost. A convolutional neural network-based identification model for TGD, built on a differentiable architecture search, is proposed in this study to employ only the CVD acquired by a portable detector for integrated identification of the type and severity level of TGDs. Second, the random oversampling method is introduced, and a strategy for applying this method is proposed to improve the poor training effect of the model caused by the natural class-imbalance problem arising from the TGD dataset. Subsequently, a comprehensive performance-evaluation metric (track geometry defect F-score) is designed by considering the actual management needs of the metro infrastructure. Finally, a case study is conducted using actual field data collected from Beijing Subway to validate the proposed model.

Urban Rail Transit Rolling Stock Scheduling Optimization with Shared Depot

This paper presents a coordinated model to optimize the rolling stock scheduling of two urban rail transit lines with a shared train depot. The proposed integrated and extended integrated optimization problems are transformed into mixed integer linear programming (MILP) problems, which can be efficiently solved by an ant colony optimization algorithm. Numerical examples based on the Beijing Subway System are implemented to demonstrate the performance of the proposed models and solution approach.

Measuring the resilience of an urban rail transit network: A multi-dimensional evaluation model

Once an urban rail transit (URT) system breaks down or is deliberately damaged, it will cause tremendous pressure on the whole urban transportation system. Hence, its ability to deal with incidents has become an important research field. Existing studies seldom consider the entire cycle of an incident and ignore changes in passenger travel behavior during emergency events. In this paper, resilience is defined as the ability of absorbing, resisting and recovering when the URT network fails. We propose a multi-dimensional evaluation model for measuring the resilience by calculating multiple abilities with consideration of multi-source data including passenger flow, train diagram, passenger travel choice behavior, network topology, etc. Finally, we present a case study based on real-world data from the Beijing Subway network to illustrate the effectiveness and applicability of the proposed model. Through the resilience analysis, we recognize the critical stations, measure emergency recovery ability and provide necessary information support for reducing the risk of URT incidents.

Weighted Complex Network Analysis of the Difference Between Nodal Centralities of the Beijing Subway System

The centrality of stations is one of the most important issues in urban transit systems. The central stations of such networks have often been identified using network to-pological centrality measures. In real networks, passenger flows arise from an interplay between the dynamics of the individual person movements and the underlying physical structure. In this paper, we apply a two-layered model to identify the most central stations in the Beijing Subway System, in which the lower layer is the physical infrastruc-ture and the upper layer represents the passenger flows. We compare various centrality indicators such as degree, strength and betweenness centrality for the two-layered model. To represent the influence of exogenous factors of stations on the subway system, we reference the al-pha centrality. The results show that the central stations in the geographic system in terms of the betweenness are not consistent with the central stations in the network of the flows in terms of the alpha centrality. We clarify this difference by comparing the two centrality measures with the real load, indicating that the alpha centrality approx-imates the real load better than the betweenness, as it can capture the direction and volume of the flows along links and the flows into and out of the systems. The empirical findings can give us some useful insights into the node cen-trality of subway systems.

Development of metro track geometry fault diagnosis convolutional neural network model based on car-body vibration data

Traditional track geometry fault diagnosis methods, such as track geometry car, track detection trolley and manual measurement, suffer from low detection frequency. Therefore, using vibration data from in-service metro vehicles to diagnose metro track geometry faults can improve the efficiency of metro track condition detection. Hence, a metro track geometry fault diagnosis model using car-body vibration was established in this study based on a convolutional neural network model, and the effects of the structural hyperparameters on the model performance were analysed. First, the problem of car-body vibration data collection was addressed, and a mileage-locating model without the global navigation satellite system was developed. Then, the metro track geometry fault diagnosis model was derived using a convolutional neural network whose architecture is determined by four hyperparameters. Finally, a case study was performed using the Beijing Subway 1 track geometry car detection data and car-body vibration data. The results demonstrated the effectiveness of the proposed model.

Application of quantum-behaved characteristic particle swarm optimisation algorithm in multi-objective optimisation of urban rail train

This paper combines operation control strategy and operation curve to address the problems of frequent switching and high energy consumption of traditional ATO control policies. Firstly, based on the Pareto principle, the objective optimisation model is established based on urban rail trains' punctuality and energy consumption. Then, a multi-objective quantum particle swarm optimisation (MOQPSO) algorithm with fewer control parameters is adopted. The Gaussian mutation operator and crowding distance sorting method are introduced to select the global optimal guidance particles better, and the optimisation effect of the conventional MOPSO algorithm is compared. Finally, the actual data of the Beijing Subway Yizhuang Line are used to verify the algorithm. Simulation results show that the MOQPSO algorithm has advantages in convergence, diversity, and optimisation. At the same time, different control strategies are compared, and the results show that the improved hybrid control strategy has a better optimisation effect on the longer line.

Experimental Study on Physical and Mechanical Properties of Sandy Cobble Strata of Beijing Subway

The aquifers of θ cobble stratum,⑦ cobble stratum and ⑨ cobble stratum are the main shallow aquifers affecting the construction of Beijing subway, and water inrush accidents often occur in the process of construction of Beijing subway. In addition, Beijing is located in a semi-arid area, the per capita water resources are less. In order to ensure the safety and protection of underground water resources in the construction of Beijing subway engineering, the typical soil samples of Beijing sandy cobble quifer were used for grain size distribution test, water content test and consolidation test. The results showed that the cobble accounted for 64.77% of the weight of the soil sample, the uniformity coefficient Cu=30.06 and the curvity coefficient Cc=0.32, which indicated that the soil was poorly graded; the water content of sand soil was 4.00%; the compression modulus of sand was 2.5~520 kPa. The test results can provide a scientific basis for remodeling the sandy cobble aquifers in Beijing subway and the simulation test of grouting water plugging and reinforcement the aquifers, and can also provide a reference for the evaluation of aquifers’ grouting effect.

Settlement characteristics of large-diameter shield excavation below existing subway in close vicinity

A case of Qinghuayuan tunnel excavation below the existing Beijing Subway Line 10 is presented. The new Qinghuayuan tunnel, part of the Beijing-Zhangjiakou High-speed Railway, was excavated by a shield machine with an outer diameter of 12.2 m. The existing subway was excavated by shallow tunnelling method. The project layout, geological conditions, reinforcement measures, operational parameters of shield machine and monitoring results of the project are introduced. During the Qinghuayuan tunnel excavation below the existing subway, total thrust, shield driving speed, cutterhead rotation speed and torque were manually controlled below the average values obtained from the previous monitoring of this project, which could effectively reduce the disturbance of the surrounding soil induced by shield excavation. The Gaussian fitting function can appropriately fit both the ground and the existing subway settlements. The trough width is influenced not only by the excavation overburden depth, but also by the forepoling reinforcement and tail void grouting measures.

Short-term origin-destination demand prediction in urban rail transit systems: A channel-wise attentive split-convolutional neural network method

Short-term origin–destination (OD) flow prediction in urban rail transit (URT) plays a crucial role in smart and real-time URT operation and management. Different from other short-term traffic forecasting methods, the short-term OD flow prediction possesses three unique characteristics: (1) data availability: real-time OD flow is not available during the prediction; (2) data dimensionality: the dimension of the OD flow is much higher than the cardinality of transportation networks; (3) data sparsity: URT OD flow is spatiotemporally sparse. There is a great need to develop novel OD flow forecasting method that explicitly considers the unique characteristics of the URT system. To this end, a channel-wise attentive split–convolutional neural network (CAS-CNN) is proposed. The proposed model consists of many novel components such as the channel-wise attention mechanism and split CNN. In particular, an inflow/outflow-gated mechanism is innovatively introduced to address the data availability issue. We further originally propose a masked loss function to solve the data dimensionality and data sparsity issues. The model interpretability is also discussed in detail. The CAS–CNN model is tested on two large-scale real-world datasets from Beijing Subway, and it outperforms the rest of benchmarking methods. The proposed model contributes to the development of short-term OD flow prediction, and it also lays the foundations of real-time URT operation and management.

An Improved Multi-Sensor MTI Time-Series Fusion Method to Monitor the Subsidence of Beijing Subway Network during the Past 15 Years

Land subsidence threatens the stable operation of urban rail transit, including subways. Obtaining deformation information during the entire life-cycle of a subway becomes a necessary means to guarantee urban safety. Restricted by sensor life and cost, the single-sensor Multi-temporal Interferometric Synthetic Aperture Radar (MTI) technology has been unable to meet the needs of long-term sequence, high-resolution deformation monitoring, especially of linear objects. The multi-sensor MTI time-series fusion (MMTI-TSF) techniques has been proposed to solve this problem, but rarely mentioned. In this paper, an improved MMTI-TSF is systematically explained and its limitations are discussed. Taking the Beijing Subway Network (BSN) as a case study, through cross-validation and timing verification, we find that the improved MMTI-TSF results have higher accuracy (R2 of 98% and, Root Mean Squared Error (RMSE) of 4mm), and compared with 38 leveling points, the fitting effect of the time series is good. We analyzed the characteristics of deformation along the BSN over a 15-year periods. The results suggest that there is a higher risk of instability in the eastern section of Beijing Subway Line 6 (L6). The land subsidence characteristics along the subway lines are related to its position from the subsidence center, and the edge of the subsidence center presents a segmented feature.

Comparison and Selection of Reconstruction Schemes for Transfer Channels of Zhichun Road Station in Beijing Subway Based on Pedestrian Simulation

As the most critical facility of the rail transit station, the transfer channel’s capacity and efficiency greatly affect the operational efficiency and safety of the transfer station. Due to the uncoordinated network planning and the inaccurate prediction of passenger flow, the current capacity of the transfer station at the peak of the transfer station is seriously insufficient. It is necessary to redesign and rebuild the transfer channel to meet the needs of passengers in the peak period. Taking the Zhichun Road Station of Beijing Subway as an example, this paper uses the Vissim pedestrian simulation software to establish different transfer channel reconstruction schemes due to the problems caused by insufficient capacity of the transfer channel. Based on the actual transfer and facility capabilities, comprehensive considerations are made from the transfer time, transfer distance and reconstruction costs to arrive at the best retrofit solution.CLC number: U291.69

Deep-Reinforcement-Learning-Based Energy Management Strategy for Supercapacitor Energy Storage Systems in Urban Rail Transit

The modeling complexity of the traction power system and variation of traffic conditions bring challenges for the optimization of energy management strategy for supercapacitor energy storage systems in urban rail transit. Therefore, in this paper a deep-reinforcement-learning-based energy management strategy is proposed: the energy management system is modeled as an intelligent agent, the reward function is formulated comprehensively considering the energy-saving and voltage-stabilizing effects of supercapacitor, a traction power system simulator is developed to emulate the environment, and the agent's behavior is improved in each headway through the deep Q-learning algorithm, and converges to the nearly-optimal policy. The proposed strategy is verified through simulation based on the Beijing Subway Batong Line. The study results show that it dynamically adjusts the voltage thresholds so as to better allocate the supercapacitor capacity along the time horizon. The energy-saving and voltage-stabilizing effects are significantly improved compared with the fixed-threshold strategy and genetic optimization, and demonstrating to be in close proximity to the optimal benchmark deduced from dynamic programming.

Integrated Optimal Design of Speed Profile and Fuzzy PID Controller for Train With Multifactor Consideration

Speed profile design and tracking control are two important issues to ensure the performance of the automatic train operation system. Aiming to realize the maximum optimization probability of train running, this paper constructs an integrated optimal method through speed profile design and fuzzy PID controller design. In the recommended speed profile design, three typical running strategies are included in the proposed comprehensive scheme to extend solution space, and the constructed train performance simulation is combined with NSGA-II algorithm to solve such multiobjective problem. As for recommended speed profile tracking, in order to improve the tracking performance of the train, which is a nonlinear system, a fuzzy PID controller is designed to adaptively adjust PID gains. Note that multiple indicators of punctually, energy consumption, stopping accuracy and comfort are all considered in the integrated design. Taking Beijing Subway Line 8 as the numerical example, the results show that the design of speed profile and fuzzy PID controller are both effective, and energy consumption saves about 10.40% with other better indicators compared with the original data.

Influence of Shield Propulsion on Longitudinal Soil Mass Near Subway Structure

Metro construction has important significance to the safety of the structure, based on a line existing Beijing subway, with air shaft and station structure as the background, research on soil displacement near the existing structure. The results show that: (1)The shield to the main structure from about 28.8m-7.2m (except for the shield at the center line testing point), the displacement and the shield is the same direction, the displacement changes generally decrease from the surface to the deep level, the variation is in the range of 1-5mm, with a linear distribution; (2) The displacement direction is as same as shield direction from 7.2m-3.6m, the displacement increases continuously is in the range of 5-15mm, the displacement accord with normal distribution; (3) 1 times diameter range and the shield to the existing structure of 7.2m-3.6m range should be regarded as significant influence area in the process of shield construction. The displacement of soil increases with the approach of shield, the maximum displacement point appears at the center of shield or above the top of shield of 1m. The above results can provide reference for shield driving parameters.