Rail Transit Network Research Articles

Collaborative passenger flow control for an urban rail transit network

AbstractAmid worsening passenger congestion, passenger flow control has become a major need in urban rail transit. However, existing passenger flow control strategies are fixed and do not consider the impacts of the spatial and temporal variations in passenger flow. To address the problems mentioned, a method for collaborative passenger flow control in urban rail transit is proposed. First, the temporal and spatial distribution of passengers in the network is obtained. Then, a model of collaborative passenger flow control for urban rail transit is built to reduce the number of controlled source points and controlled passengers and minimize the difference between the expected number of passengers on bottleneck links and the number of passengers with control. Next, a multilevel network that considers the time dimension is established based on the model. A forward–backward algorithm is introduced to make full use of the adaptive learning rate to solve the model. A case study based on a small‐scale network shows that the forward–backward algorithm has good convergence. To verify the effectiveness of the method, the passenger flow of Chengdu Metro during COVID‐19 is analyzed. The objective function of the forward–backward algorithm is 44% lower than that of the gradient descent method and 36% lower than that of adaptive moment estimate (ADAM). Its computational speed is also acceptable. The results show that compared with the fixed passenger flow control strategy of metro operators, the obtained strategy is more effective in reducing the number of controlled passengers and source points and alleviating congestion of bottleneck links.

Adjusting Train Timetables Based on Passenger Flow for Cross-Line Trains

Urban rail transit becomes increasingly convenient for passengers by networking single lines. The urban rail transit network with cross-line trains has different characteristics from the traditional network. The trains can run to different lines at the crossing station, and the operation delay may be spread to different lines because of the operation schedule. A model for adjusting the running delay of cross-line trains is proposed in this paper. The optimization objective is to minimize the total delay time and the number of stranded passengers. In this paper, different scales of delay are analyzed and the operation order of the trains is allowed to be changed at the crossing station. Case simulations achieve good optimization results on the total delay time and the number of stranded passengers. Optimization diagrams are given under different strategies. The model and methodologies are also applied to Beijing Metro Line 13, and good simulation results are achieved for different scales of delay.

The Economic Value of Reserve Capacity Considering the Reliability and Robustness of a Rail Transit Network

The Economic Value of Reserve Capacity Considering the Reliability and Robustness of a Rail Transit Network

Line-Specific Energy Modeling Framework for Urban Rail Transit Systems: A Case Study of Boston

Urban rail transit systems play an essential role in improving mobility and efficiency. A complex rail transit network serves the Boston metropolitan area, U.S., which costs $38 million for the 422 GWh of system electricity consumed annually. With the aim of developing a tool for energy and cost reduction decision support, we propose a comprehensive machine learning framework to investigate line-specific contributions to energy. This effort builds on prior work in estimating a system-wide energy model for the Boston network. By introducing line-specific train movement and operation variables, we obtain a higher-performing model [Formula: see text]. Furthermore, the model better explains the relationship between energy and train movement, ridership, and weather variables. Most importantly, the model facilitates analyses of how each line contributes to system consumption at the hour level. We found that the non-line-specific variables made a contribution of −2.7% to the average hourly energy of consumption of −5.4 MWh with a baseline energy consumption of 39 MWh. The Red Line dominates the energy consumption among line-specific variables, contributing 2.3% to the hourly average. Our model could be further enhanced to evaluate the energy and cost impacts of line-specific strategies that may be required for future planning and disaster response, as well as for real-time energy monitoring by line.

Synchronization of train timetables in an urban rail network: A bi-objective optimization approach

As urban rail networks in big cities tend to expand, the synchronization of trains has become a key issue for improving the service quality of passengers because most urban rail transit systems in the world involve more than one connected line, and passengers must transfer between these lines. In contrast to most existing studies that focus on a single line, in this study, we focus on synchronized train timetable optimization in an urban rail transit network, considering the dynamic passenger demand with transfers as well as train loading capacity constraints. First, we propose a mixed-integer programming (MIP) formulation for the synchronization of training timetables, in which we consider the optimization of two objectives. The first objective is to minimize the total waiting time of passengers, involving arriving and transfer passengers. Our second objective is a synchronization quality indicator (SQI) with piecewise linear formulation, which we propose to evaluate the transfer convenience of passengers. Subsequently, we propose several linearization techniques to handle the nonlinear constraints in the MIP formulation, and we prove the tightness of our reformulations. To solve large-scale instances more efficiently, we also develop a hybrid adaptive large neighbor search algorithm that is compared with two benchmarks: the commercial solver CPLEX and a metaheuristic. Finally, we focus on a series of real-world instances based on historical data from the Beijing metro network. The results show that our algorithm outperforms both benchmarks, and the synchronized timetable generated by our approach reduces the average waiting time of passengers by 1.5% and improves the connection quality of the Beijing metro by 14.8%.

Syncretic K-shell algorithm for node importance identification and invulnerability evaluation of urban rail transit network

In this paper, the Syncretic K-shell algorithm is developed and applied to identify the node importance and evaluate the invulnerability in an urban rail transit network, which is based on the idea that the nodes removed later are more important than those removed former, the nodes with the same core value also have different importance. It has a very important strategic significance for optimizing network structure, improving operational efficiency, and providing targeted protection and designated accident prevention measures for stations with different importance. Syncretic Core Value, as the node importance recognition criterion of Syncretic K-shell algorithm, considers the influence of node degree, cumulative core value and syncretic weight of adjacent nodes on node importance. Firstly, we use space L to establish the indirect and non-weighted urban rail transit network. Then, the node importance recognition steps of Syncretic K-shell algorithm are introduced. Then, a numerical study of the proposed Syncretic K-shell algorithm is conducted, and the Degree Centrality, Betweenness Centrality, Closeness Centrality, Mixed Degree Decomposition and Classical K-shell are used as comparison approaches. The node anti-destruction under node failure (including intentional attack and random attack) is evaluated, and finally, an example study is conducted with the background of Chengdu Metro Network. The results show that the important nodes identified by Syncretic K-shell have a greater impact on anti-destruction performance. The research found that Chengdu Metro Network is more vulnerable to attack and easy to be destroyed under intentional attack. Finally, two coping strategies are provided. According to the research results, we can conclude that compared with the previous node importance recognition methods, Syncretic K-shell algorithm has five advantages: (i) it can distinguish the importance between nodes with the same core value; (ii) considering the global and local centrality of nodes, the Syncretic Core Value of nodes can better reflect the importance of nodes; (iii) it can recognize the importance of nodes in networks with the same degree value or without obvious aggregation characteristics; (iv) the time complexity of Syncretic K-shell algorithm can be used for the recognition of node importance in large-scale networks; (v) it can better distinguish and amplify the differences between nodes, and quickly and effectively rank the importance of nodes.

Research on the Scheme Comparison of Xuzhou Urban Rail Transit Network Based on Network Form

The traditional urban rail network planning scheme comparison lacks the advantages and disadvantages of different schemes from the perspective of network form. In this paper, from the perspective of network form, a comparison scheme of urban rail transit network planning is proposed. Based on graph theory and complex network theory, this paper proposes quantitative indicators from the aspects of network development level, network form, and structure efficiency, and constructs an index system for comparing rail transit planning schemes. Taking Xuzhou urban rail transit planning network as an example, this paper analyzes the comparison between the two planning schemes and the original planning scheme. The results show that there are some differences among the three schemes in basic indexes and macro characteristic indexes. From the perspective of network form, scheme II is the best, the scheme I is the second, and both are optimized to the previous scheme.

How Rail Transit Makes a Difference in People’s Multimodal Travel Behaviours: An Analysis with the XGBoost Method

The rail transit system was developed in Chinese large cities to achieve more efficient and sustainable transport development. However, the extent to which the newly built rail transit system can facilitate people’s multimodality still lacks evidence, and limited research examines the interrelationship between trip stages within a single trip. This study aims to explore the interrelations between trip stage characteristics, socio-demographic attributes, and the built environment. It examines how rail transit is integrated as part of multimodal trips after it is introduced. The data are extracted from the Chongqing Urban Resident Travel Survey from 2014, three years after the new rail transit network was established. It applies an XGBoost model to examine the non-linear effect. As a result, the separate trip stage characteristics have more of an impact than the general trip characteristics. The non-linear effects revealed by the machine learning model show changing effects and thresholds of impact by trip stage characteristics on people’s main mode choice of rail transit. An optimal radius of facility distribution along the transit lines is suggested accordingly. Synergistic effects between variables are identified, including by groups of people and land use characteristics.

Simulation-Based Schedule Optimization for Virtual Coupling-Enabled Rail Transit Services with Multiagent Technique

Virtual coupling (VC) is a train-centric next generation signalling system, which can enable multiple trains to operate in a formation just like one train or decouple separately on-the-run or at station flexibly or as planned. With the aim of optimizing the interdeparture train headway time, providing the variable capacity for diverse passenger demand, maximizing the passenger riding comfort degree, and minimizing passenger travel cost and train operation cost, the dynamic schedule for VC-enabled rail transit services is investigated with the multiagent simulation technique on NetLogo platform. Our contribution is mainly fourfold. First, VC-enabled rail transit entity for simulation is represented with the multiagent technique, including representation of train unit, train convoy, passenger attributes and behavior, and mathematical formula for calculation of the train operation cost and passenger travel cost, as well as passengers riding comfort degree are proposed. Second, the operational principles for flexible and self-organisingVC-enabled trains are defined. Third, the VC-enabled train-centric, passenger demand-driven, and agent-based simulation flow and algorithms are developed innovatively, which adopt the ergodic strategy for simulation by traversing each O-D pair demand along each route section over the rail transit network. Finally, we test and discuss the proposed methodology on the designed computational experiment on the NetLogo platform, and the simulation results series validated the effectiveness of the proposed methodology. The provided research can effectively support the VC-enabled platoon operation-oriented train service schedule for future study.

Simulation-Based Method for the Calculation of Passenger Flow Distribution in an Urban Rail Transit Network Under Interruption

In the extensive urban rail transit network, interruptions will lead to service delays on the current line and spread to other lines, forcing many passengers to wait, detour, or even give up their trips. This paper proposes an event-driven simulation method to evaluate the impact of interruptions on passenger flow distribution. With this method, passengers are regarded as individual agents who can obtain complete information about the current traffic situation, and the impact of the occurrence, duration, and recovery of interruption events on passengers’ travel decisions is analyzed in detail. Then, two modes are used to assign passenger paths: experience-based pre-trip mode and response-based entrap mode. In the simulation process, the train is regarded as an individual agent with a fixed capacity. With the advance of the simulation clock, the network loading is completed through the interaction of the three agents of passengers, platforms, and trains. Interruption events are considered triggers, affecting other agents by affecting network topology and train schedules. Finally, taking Chongqing Metro as an example, the accuracy and effectiveness of the model are analyzed and verified. And the impact of interruption on passenger flow distribution indicators such as inbound volume, outbound volume, and transfer volume is studied from both the individual and overall dimensions. The results show that this study provides an effective method for calculating the passenger flow distribution of an extensive urban rail transit network in the case of interruption.

A novel method to identify influential nodes based on hybrid topology structure

With the increasing popularity of rail transit and the increasing number of light rail trips, the vulnerability of rail transit has become increasingly prominent. Once the rail transit is maliciously broken or the light rail station is repaired, it may lead to large-scale congestion or even the paralysis of the whole rail transit network. Hence, it is particularly important to identify the influential nodes in the rail transit network. Existing identifying methods considered a single scenario on either betweenness centrality (BC) or closeness centrality. In this paper, we propose a hybrid topology structure (HTS) method to identify the critical nodes based on complex network theory. Our proposed method comprehensively considers the topology of the node itself, the topology of neighbor nodes, and the global influence of the node itself. Finally, the susceptible–infected–recovered (SIR) model, the monotonicity (M), the distinct metric (DM), the Jaccard similarity coefficient (JSC), and the Kendall correlation coefficient (KC) are utilized to evaluate the proposed method over the six real-world networks. Experimental results confirm that the proposed method achieves higher performance than existing methods in identifying networks.

Modeling and Efficient Passenger-Oriented Control for Urban Rail Transit Networks

Real-time timetable scheduling is an effective way to improve passenger satisfaction and to reduce operational costs in urban rail transit networks. In this paper, a novel passenger-oriented network model is developed for real-time timetable scheduling that can model time-dependent passenger origin-destination demands with consideration of a balanced trade-off between model accuracy and computation speed. Then, a model predictive control (MPC) approach is proposed for the timetable scheduling problem based on the developed model. The resulting MPC optimization problem is a nonlinear non-convex problem. In this context, the online computational complexity becomes the main issue for the real-time feasibility of MPC. To reduce the online computational complexity, the MPC optimization problem is therefore reformulated into a mixed-integer linear programming (MILP) problem. The resulting MILP problem is exactly equivalent to the original MPC optimization problem and can be solved very efficiently by existing MILP solvers, so that we can obtain the solution very fast and realize real-time timetable scheduling. Numerical experiments based on a part of Beijing subway network show the effectiveness and efficiency of the developed model and the MILP-based MPC method.

Cartographic Modelling and Multi-Criteria Analysis (CMCA) for Rail Transit Suitability

Rail transportation planning is a continuous, multi-phase, functioning scale that is based on reconstructing the spatial system, thus contributing to build a more quality-focused, long-term-oriented and balanced approach. This paper presents cartographic modelling and multi-criteria analysis (CMCA) for the optimum route/station location in accordance with selected criteria and constraints for rail transit system planning. The methodology is based on overlay analysis (cartographic modelling) to combine diverse criteria maps and the analytic hierarchy process (AHP) method for criteria weighting. The purpose of the study is to examine a GIS multiple-criteria decision-making (MCDM) framework by considering Stream, Geology, Slope, Land use and Population decision criteria. The decision modelling framework conceives an additional prominent approach by co-producing with the public, especially for underserved areas of low socio-economic status and public user expectations in the railway transport planning process. This model applied to a candidate metro line referred to as M 18 (Başakşehir-Esenyurt-Beylikdüzü) with eight railway stations for the preliminary stage evaluation. The outcomes of this research will close the gap by establishing a novel suitability framework which can be used as a preliminary analysis for transforming and developing urban rail transit networks.

Evaluation Method for Node Importance of Urban Rail Network Considering Traffic Characteristics

As a sustainable means of public transport, the safety of the urban rail transit is a significant section of public safety and is highly important in urban sustainable development. Research on the importance of urban rail stations plays an important role in improving the reliability of urban rail networks. This paper proposed an improved method for evaluating the importance of urban rail stations in a topology network, which was used to identify the key stations that affect the urban rail network performance. This method was based on complex network theory, considering the traffic characteristics of the urban rail network that runs on specific lines and integrating the structural characteristics and interrelationship of the lines where the stations are located. Hereafter, this method will be abbreviated as CLI. In order to verify that the high importance stations evaluated by this method were the key stations that had a great impact on the urban rail network performance, this paper designed a comparative attack experiment of betweenness centrality and CLI. The experiment was carried out by taking the Suzhou Rail Transit (SZRT) network as an example and the largest connected subgraph as well as the network efficiency as indicators to measure the network performance. The results showed that CLI had a greater impact on network performance and could better evaluate the key stations in the urban rail network than node degree and betweenness centrality.

Forecast of Short-Term Passenger Flow in Multi-Level Rail Transit Network Based on a Multi-Task Learning Model

With the refinement of the urban transportation network, more and more passengers choose the combined mode. To provide better inter-trip services, it is necessary to integrate and forecast the passenger flow of multi-level rail transit network to improve the connectivity of different transport modes. The difficulty of multi-level rail transit passenger flow prediction lies in the complexity of the spatiotemporal characteristics of the data, the different characteristics of passenger flow composition, and the difficulty of research. At present, most of the research focuses on one mode of transportation or the passenger flow within the city, while the comprehensive analysis of passenger flow under various modes of transportation is less. This study takes the key nodes of the multi-level rail transit railway hub as the research object, establishes a multi-task learning model, and forecasts the short-term passenger flow of rail transit by combining the trunk railway, intercity rail transit and subway. Different from the existing research, the model introduces convolution layer and multi-head attention mechanism to improve and optimize the Transformer multi-task learning framework, trains and processes the data of trunk railway, intercity railway, and subway as different tasks, and considers the correlation of passenger flow of trunk railway, intercity railway, and subway in the prediction. At the same time, a new residual network structure is introduced to solve the problems of over-fitting, gradient disappearance, and gradient explosion in the training process. Taking the large comprehensive transportation hub in Guangzhou metropolitan area as an example, the proposed multi-task learning model is evaluated. The improved Transformer has the highest prediction accuracy (Average prediction accuracy of passenger flow of three traffic modes) 88.569%, and others methods HA, FC-LSTM and STGCN are 81.579%, 82.230% and 81.761%, respectively. The results show that the proposed multi-task learning model has better prediction performance than the existing models.

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.

Assessing Spatial Synergy Between Integrated Urban Rail Transit System and Urban Form: A BULI-Based MCLSGA Model With the Wisdom of Crowds

Spatially synergizing the urban rail transit (URT) network integrated with its feeder transit system and the urban form plays an important role in improving the effectiveness of URT in mitigating traffic congestion, reducing air pollution, optimizing urban spatial structure, etc. This article mainly focuses on assessing the spatial synergy between the two parts by specifying the assessment criteria that can effectively characterize the spatial synergic mechanism between the two parts and developing a novel multicriteria large-scale group assessment (MCLSGA) model, in which basic uncertain linguistic information (BULI), as an extended form of fuzzy linguistic approach, is used to model and process the subjective assessment information (Assess-Inf) elicited by experts. In order to alleviate the computing complexity, an agglomerative hierarchical clustering algorithm is introduced to cluster the Assess-Inf given by a huge number of experts as per the organizers’ expected discrimination level on reliability-related Assess-Inf. A method for weighting the clusters is then proposed to control the roles played by the items of the Assess-Inf, representing each cluster and having different reliability levels in their fusing process to tap into wisdom of crowds (WOC) while accommodating the organizers’ trust level in the reliability-related Assess-Inf given by experts. Afterward, the best–worst method is extended to the BULI-based large-scale group assessment context with the aim of accurately weighting the criteria by drawing on WOC. Finally, a case study on assessing the spatial synergy between Chongqing’s integrated URT system and urban form is conducted to validate the validity of the proposed MCLSGA model.

Research on evolution dynamics of urban rail transit network based on allometric growth relationship

Each stage of the construction of the rail transit network has unique dynamic characteristics. It can provide recommendations for rail transit network planning and phased construction by evaluating the degree of evolution of the urban rail transit network and dividing the evolution stages precisely. Starting from the allometric growth relationship between transfer nodes and ordinary nodes in the urban rail transit network, this paper defines the evolution level of the rail transit network and the growth rate difference between transfer nodes and ordinary nodes and deduces the dynamic logistic equation of URT network evolution level using the mathematical derivative and logarithmic relationship. This paper reveals the dynamic law of the evolution process of the urban rail transit network at a theoretical level, analyzes the dynamic evolution process on this basis, determines the threshold for phasing the evolution stage, and divides it into four evolution stages. Finally, the results of the theoretical derivation are validated by the evolution data of the Beijing Rail Transit Network (1984–2020) over 45 years. The verification results are substantially congruent with the conclusions of the theoretical derivation, demonstrating the accuracy of the theoretical model and its practical usefulness in directing the building of the rail transit network.

Simulation-Based Optimization of Transport Efficiency of an Urban Rail Transit Network

It has been proven that developing urban rail transit (URT) is the key to solving the traffic problems in big cities, and transport efficiency is essential for scientific line planning and construction, operation management and sustainable development of urban rail transit. This paper focuses on the transport efficiency of the URT network and adopts shadow efficiency theory creatively to combine factors of passengers, capacity, and efficiency effectively. A quantitative calculation method of system utility based on “double time penalty” is proposed. According to the characteristics of Beijing URT, this paper puts forward a single optimization strategy and combinatorial optimization strategy and analyzes the results with DEA. The simulation of Beijing subway network proves the availability of the method and its optimization effects.

Adaptive rail transit network operations with a rollout surrogate-approximate dynamic programming approach

This paper presents an adaptive optimization framework for dynamic rail transit network operations with use of a rollout surrogate-approximate dynamic programming method. The optimization algorithm derives coordinated decisions of service schedules and train unit deployment with respect to prevailing passenger demand. Considering the computational effectiveness needed for real-time applications, a state-dependent surrogate function is incorporated to approximate the costs associated with operational decisions over future stages. The surrogate approximation is updated iteratively via a temporal difference learning process with feeding of observations made from the transit network. The proposed framework is implemented and tested on a real-world scenario in Hong Kong Light Rail Transit (LRT) network. The results reveal that the proposed framework is able to reduce significantly the total passengers’ waiting times over existing plans with reasonable computational time via use of the surrogate approximation. This suggests the potential of the proposed optimizer for real time applications in large-scale rail transit networks.