Urban Rail Transit Management Research Articles

Physics Guided Deep Learning-Based Model for Short-Term Origin–Destination Demand Prediction in Urban Rail Transit Systems Under Pandemic

Accurate origin–destination (OD) demand prediction is crucial for the efficient operation and management of urban rail transit (URT) systems, particularly during a pandemic. However, this task faces several limitations, including real-time availability, sparsity, and high-dimensionality issues, and the impact of the pandemic. Consequently, this study proposes a unified framework called the physics-guided adaptive graph spatial–temporal attention network (PAG-STAN) for metro OD demand prediction under pandemic conditions. Specifically, PAG-STAN introduces a real-time OD estimation module to estimate real-time complete OD demand matrices. Subsequently, a novel dynamic OD demand matrix compression module is proposed to generate dense real-time OD demand matrices. Thereafter, PAG-STAN leverages various heterogeneous data to learn the evolutionary trend of future OD ridership during the pandemic. Finally, a masked physics-guided loss function (MPG-loss function) incorporates the physical quantity information between the OD demand and inbound flow into the loss function to enhance model interpretability. PAG-STAN demonstrated favorable performance on two real-world metro OD demand datasets under the pandemic and conventional scenarios, highlighting its robustness and sensitivity for metro OD demand prediction. A series of ablation studies were conducted to verify the indispensability of each module in PAG-STAN.

Analyzing the Impacts of Land Use and Network Features on Passenger Flow Distribution at Urban Rail Stations from a Classification Perspective

This study employed big data analytics to investigate the impacts of land use and network features on passenger flow distribution at urban rail stations. The aim was to provide decision support for differentiated operational management strategies for various types of rail stations, thereby achieving refined operation and the sustainable development of urban rail systems. First, this study compared clustering results using different similarity measurement functions within the K-means algorithm framework, selecting the optimal similarity measurement function to construct clustering models. Second, factors influencing passenger flow distribution were selected from land use and network features, forming a feature set that when combined with clustering model results, served as input for the XGBoost model to analyze the relationship between various features and the station passenger flow distribution. The case study showed that (1) the clustering results using a dynamic time-warping distance as the similarity measurement function was optimal; (2) the results of the XGBoost model highlighted commercial services and closeness centrality as the most important factors that affected rail station passenger flow distribution; (3) urban rail stations in Nanjing could be categorized into four types: “strong traffic attraction stations”, “balanced traffic attraction stations”, “suburban strong traffic occurrence stations”, and “distant suburban strong traffic occurrence stations”. Differentiated operational and management strategies were developed for these station types. This paper offers a novel approach for enhancing the operational management of urban rail transit, which not only boosts operational efficiency but also aligns with the goals of sustainable development by promoting resource-efficient transportation solutions.

Learning spatial–temporal pairwise and high-order relationships for short-term passenger flow prediction in urban rail transit

Short-term passenger flow prediction (STPFP) helps ease traffic congestion and optimize urban rail transit (URT) system resource allocation. Although graph-based models have been widely used in STPFP because of their ability to model complex spatial–temporal dependencies, accurate STPFP still faces challenges. The graph-based methods exploit pairwise stations to model spatial dependencies without considering the non-pairwise high-order relationships between multiple stations. Besides, the event’s occurrence will make the passenger flow at a particular moment have a more profound impact on the passenger flow at a future moment. To solve these issues, a spatial–temporal hypergraph attention recurrent network (STHGARN) method is proposed for STPFP. Specifically, STHGARN first models passenger flow data’s recent, daily, and weekly characteristics through three parallel components. Then, each parallel component uses the hypergraph attention recurrent network (HGARN) cell to jointly learn the pairwise and high-order relationships of multiple stations. Finally, a temporal Hawkes attention mechanism is constructed and concatenated after each parallel HGARN cell to simulate the influence of events and capture the historical key moments that affect the future passenger flow. Numerous experiments on three metro ridership datasets show that STHGARN is superior to the most advanced methods, and good prediction results can lay the foundation for the operation and management of URT.

A comprehensive analysis method of urban rail transit operation accidents and safety management strategies based on text big data

Although the occurrence of urban rail transit operation accidents threatens the safety of people's lives and property, there is little research on how to systematically mine the knowledge behind accident data in China, and there is also lack of methods to support the safety management strategy of urban rail transit operation. To address these shortcomings, this study proposes a comprehensive analysis method that combines qualitative and quantitative analysis of urban rail operation accident factors and safety management strategies based on accident data, which can provide reference for systematic analysis of such accidents in the future and will help urban rail safety researchers and decision makers to take effective safety management measures. The new contributions of the method are as follows: (1) a semantic network framework for accidents and a conceptual model of accident risk control chain are proposed; (2) a method for acquiring and constructing key contents based on semantic analysis is elaborated, and indicators for evaluating the influence degree of key item and the influence possibility of entries are proposed; (3) a management matrix is established to serve safety decision making based on indicators for the degree of attention that key item of accidents should receive. In order to explain the application of the comprehensive analysis method, 910 urban rail transit operation safety accident cases are analyzed in detail, and suggestions for the actual operation management of urban rail transit are proposed in the light of the actual situation of urban rail transit operation in China.

Research on construction data association management of Urban Rail Transit Based on BIM

With the continuous expansion of the scale of urban rail transit construction projects, it is difficult to improve the coordination in construction and the relevance of construction data. In order to solve the problem of data association in construction, an urban rail transit construction data association management system oriented to BIM Technology is proposed. BIM is used to build a dynamic workflow collaboration architecture to manage the data in urban rail transit construction, so as to realize construction collaborative scheduling. In the test and analysis of the system, the comprehensive ability test of the system shows that the minimum response time of the system can be reduced to 1.2 ms. At the same time, the system has ideal visualization effect and obvious practical value in practical application. Therefore, the urban rail transit construction data association management system based on BIM can show ideal results in complex construction environment, which is of great significance to China's urban construction.

Relationship analysis of short-term origin–destination prediction performance and spatiotemporal characteristics in urban rail transit

Accurately predicting passengers’ origin and destination (OD) demand and analyzing their spatiotemporal characteristics are the key to efficient operation and management of urban rail transit. While obtaining these spatiotemporal characteristics and thus making a short-term OD prediction are a big challenge for a model due to its high dimensionality and uncertainty. This paper proposes a pattern match algorithm based on t-distribution stochastic neighbor embedding and K nearest neighbors (TSNE-KNN) to promote the prediction performance and introduces similarity indicators to explore these features of OD flow and their relationship with the forecasting performance. Analysis of automatic fare collection data of Beijing rail transit shows that the TSNE-KNN model is superior to other state-of-the-art approaches, even including deep neural network models, and the similarity, which is affected by the functional attributes of station, the surrounding land use attributes and the degree of development of the road network, can be a universal indicator to indirectly reflect the time–space properties of the OD flow. It is found that as the similarity of daily OD flows decreases, the performance of short-term OD prediction of decreases, and rail transit stations are gradually shifting from the periphery to the center of the city.

Analysis on Bidding Management of Urban Rail Transit

Analysis on Bidding Management of Urban Rail Transit

Research on Safety Management Model and Emergency Management of Urban Rail Transit

Research on Safety Management Model and Emergency Management of Urban Rail Transit

The Use of Artificial Intelligence in the Construction of Operation Safety Early Warning Management System of Urban Rail Transit

In order to reduce the risk of safety management of urban rail transit (URRT) and improve the safety early warning of URRT, artificial intelligence (AI) is used to construct the early warning management system of URRT. Firstly, the four factors that affect the safety of URRT are discussed, the URRT system is divided into several modules, and the functions of each module are introduced. Secondly, the safety early warning analysis module is designed based on neural network technology to make it have the ability of safety early warning. Finally, the security prediction system is tested. The results show that the overall prediction error of probabilistic neural network algorithm used is less than 5%, and the classification accuracy of PNN is about 50%, which can achieve excellent prediction of data. The safety early warning system is used to predict the passenger flow of a station, and compared with the actual passenger flow, the prediction model has good prediction accuracy and credibility. Therefore, the safety early warning management system of URRT designed can meet the needs of practical application. This paper provides a reference for the safety management of URRT.

Study on Passenger Flow Assignment Method of Urban Rail Transit Based on Improved Logit Model

With the advantages of small pollution, punctuality, safety and large capacity, urban rail transportation provides good ideas for solving China’s urban transportation problems and gradually becomes an indispensable part of China’s urban public transportation system. According to the current development of urban rail transportation, the era of networked operation has arrived, and the connectivity between different stations in the metro network and the mobility of operation and management have greatly increased. And for passengers, considering the convenience of seamless transfers, the complexity of the line network structure, and the rapid feedback of real-time passenger flow information on the line, it will cause the passengers’ travel behavior to be highly random. Different passengers are choosing the route selection scheme to reach the target station will also make the spatial and temporal distribution within the rail transit network more diverse and complicated. This paper adopts the method of analyzing the behavior of passenger travel route selection under the established urban rail transit network to establish a generalized cost function and Logit model, and contributes to the planning, design and operation management of urban rail transit.

Online estimation model for passenger flow state in urban rail transit using multi‐source data

AbstractThe estimation of present passenger flow state plays a vital role in the urban rail transit (URT) operation process and it is the basis of passenger flow control and train dispatching. Most of the existing researches used the Automatic Fare Collection (AFC) System data as the single data source for estimating present passenger flow state. In consideration of the delay in the data uploading process, the obtained estimation results based on the single AFC data source are not accurate enough, which could lead to the improper handling and decision failure and cause high risk and low efficiency for the operation and management of URT. To address problems mentioned above, this paper adopted the automatic differentiation method as the framework for the fusion of multi‐source heterogeneous data (including uploaded AFC data, the mobile phone signaling data provided by mobile phones, and the historical passenger flow data, etc.). Then, this paper proposed the online estimation model and the error layered optimization algorithm to implement the estimation of present passenger flow state. The calculation results indicate that the proposed model and algorithm can obtain reasonable and reliable URT passenger flow state. Finally, the study developed a present passenger flow state estimation system using the proposed model and algorithm. The system has been deployed in Chengdu Metro that serves about 3 million passengers per day from October 2018 to April 2020, with the overall error within 3% as of now.

Emergency Response for COVID-19 Prevention and Control in Urban Rail Transit Based on Case-Based Reasoning Method

As an important public travel mode, urban rail transit has the characteristics of crowded passengers and closed operation. Safe management of urban rail transit is an important research topic that attracted attention in recent years. This article proposes a decision analysis method based on case-based reasoning, which aims to solve the emergency response problems for the prevention and control of corona virus disease 2019 (COVID-19) in urban rail transit. In this method, first, the historical cases are extracted and filtered by calculating the similarity between the target case and the historical case. A set of similar historical cases is constructed by setting the similarity threshold in advance. Second, comprehensive utility value of emergency response of each similar case is calculated referring to the utility evaluation of emergency response effect and response cost of each similar historical case. On this basis, the emergency plan of the target case is generated by selecting the emergency plans of the similar historical cases corresponding to the maximum comprehensive utility values of the emergency responses. Finally, with the emergency responses of COVID-19 in Tianjin rail transit as the background, this paper explains the feasibility and effectiveness of the proposed method within a case study.

The Predictability of Short-Term Urban Rail Demand: Choice of Time Resolution and Methodology

The accuracy of short-term demand forecasting is critical for real-time operation management of urban rail transit, which largely depends on the choice of time resolution. Although there have been continuous improvements in forecasting models, the basic issue has not been well addressed. In this regard, the predictability of short-term demand in terms of time resolution setting and the corresponding model selection have been addressed in this study. Two methods have been considered: the demand forecasting with the past demand during the same time slot on the same weekday (the same period method); and that with continuous time series demand exactly before the forecasted time slot (time series method). The predictability for these two methods was respectively measured by the similarity of the same period and the stability of the time series. Consequently, the influence of time resolution on the predictability of short-term demand for urban rail transit has been evaluated. With the methods proposed, this study conducted an analysis on five-week smartcard data in the Beijing subway system. Results suggest that the predictability of short-term demand presented remarkable heterogeneity in both time and space. The predictability of demand forecasting at station level has been summarized into different levels, and the corresponding methods can be selected for each class. Generally, to ensure a desirable accuracy, forecasting can be made at a 10-min and 60-min interval on weekdays and weekends, respectively. The same period method works better for the short-term demand forecasting on weekdays. While the time series method performs better for prediction on weekends. As for short-time OD (origin-destination) demand, the time series method with a 10-min interval, which is supplemented by the same period method, can generate acceptable forecasting results. In brief, this study provides suggestions on the time resolution and method selection for short-term demand forecasting.

Estimation of passenger route choices for urban rail transit system based on automatic fare collection mined data

When multiple routes connect the same origin and destination (OD), passenger route choices are related to the operation and management of urban rail transit, including the design of the train plan, fare clearing, and passenger distribution. The passengers’ travel data from an automatic fare collection (AFC) system provides useful data for the analysis of passenger route choices. We propose an estimation method of passenger route choices based on AFC data, which includes passenger’s travel time and OD. In this paper, the AFC data used for analysis were mined to eliminate unreasonable individual travel data. In this study, the components of passenger travel times and their influencing factors are analyzed and the results indicate that the travel times are related to the route choices of passengers. Cluster analysis indicated that passenger travel times follow the logarithmic normal distribution. Based on this and a multi-route distribution sample, an estimation model for passenger route choice proportions is proposed. Furthermore, experimental results show that the data mining method has high accuracy and the result also supported the hypothesis about the travel time distribution. The model was applied to the Chengdu metro, which is one of the largest metro systems in the world, and the obtained results indicate that the model works well. In addition to the initial application, the estimation method provides a new method to calculate the route choice proportion for urban rail transit with maximum dependability and timeliness.

Emergency disposal of urban rail transit based on distributed workflow

Under the background of the network operation of urban rail transit, the rapid expansion of rail network structure in various cities, on the one hand, has promoted the rapid development of rail transit industry. On the other hand, it put tremendous pressure on the daily operation and management of rail transit operators. The traditional centralized disposal mode is not in a position to meet the increasingly complex network and passenger demand any more. In this paper, based on the distributed workflow theory, a distributed multi-aircraft collaborative system framework is established for emergency management of urban rail transit and an emergency response model, using Oracle database management system as the main technical to realize the digital management of contingency plans, the theory is finally applied in practice, and the design of the urban rail transit emergency response system for OCC layer is realized.

Cost-benefit analysis for regenerative energy storage in metro

PurposeRegenerative braking is an efficient energy saving technology in urban rail system, in which the recovery energy from braking trains is collected by some equipments and released to accelerating trains when needed. However, the high cost and low lifetime of storage devices prevent the widespread use of this technology. The purpose of this paper is to conduct thorough cost-benefit analysis to facilitate China’s urban rail companies to make decisions on the use of such technology.Design/methodology/approachTo evaluate the benefit from regenerative energy storage, the authors formulate an improved integrated scheduling and speed control model to calculate the net energy consumption associated with different energy recovery rates and then define the benefit as the amount of energy saving arising from the usage of storage equipments. With the frequent charge/discharge operations on storage equipments, the energy recovery rate generally decreases which lowers the benefit, but the maintenance cost increases. By trading-off benefit and cost, the authors derive the optimal scrapping time, the maximum profit and the profitability condition for storage devices.FindingsSimulation studies based on the Beijing Metro Yizhuang Line of China are given. The results show that compared with the current timetable and speed profile, the integrated scheduling and speed control approach with energy recovery rate of 0.5 can reduce the net energy consumption by 12.69 per cent; the net energy consumption can be well approximated as a linear function of energy recovery rate; and the maximum profit and the optimal scrapping time on regenerative energy storage devices are both positively related to the electricity price. The allowance proportion and the number of service trains such that busy lines with higher electricity price or allowance proportion have advantages to use the regenerative energy storage devices.Research limitations/implicationsIn this work, a linear energy recovery rate and a linear maintenance cost are used in the cost-benefit analysis process. In future research, the more accurate expressions on energy recovery rate and maintenance cost should be considered if more data on recovery rate and maintenance cost can be gathered.Originality/valueThe main values of this paper are to develop the integrated optimization approaches for train scheduling and speed control and, on this basis, make thorough cost-benefit analysis for regenerative energy storage to improve the operations management of urban rail transit.

The safety management of urban rail transit based on operation fault log

With the rapid development of China's rail transit, the safety of Metro has roused the society's concern more and more widespread. So deeply mining the massive dispatching log data is of great significance to the safety management of Metro operation. For the purposes of risk early-warning and promoting the safety management level of Metro, a dispatching fault log management and analysis database system (DFLMIS) is designed, which contains almost all kinds of accidents that have occurred in the operation of Metro. Taking the compatibility and safety into consideration, the Visual studio 2010 and SQL-sever 2005 are used to develop the DFLMIS. First, changing operation fault log is regarded as a state machine, which describes the data from three dimensions: time, value of information, frequency, and forms the operation scheduling database with data management as the visual angle. Second, the probability space cut algorithm is presented for pruning strategy of probability space, which is suitable for high frequent update of the environment of grid technology as index structure. Finally, the procedures are demonstrated to how DFLMIS can be used to early-warn and identify the risk sources. The research and design of DFLMIS would be of great help to the Metro operators to identify the risk and promote the safety management level.

Decision Support Technology Research of Emergency Disposal

This paper focuses on the problem about how to efficiently process the emergency of rail transit and guarantee the lowest accident loss in a short period of time, which is the urban rail transit management policy that makers are faced with, and which develops a high integrated system with strong information based on contingency plans to give the decision aid of urban rail transit emergency events. The paper uses formal methods to present the emergency plan, generate the emergency disposal plan, meet the requirements of on-site emergency disposal, and it realizes the modernization of urban rail transit emergency management which has an important significance. Finally, taking a subway fire as an example, it describes the practicality of the auxiliary decision system.

Braking Performances of Urban Rail Trains

In the line design and operation management of urban rail transit, braking performance of trains is a complicated but important part. This study analyzes the braking performances of urban rail trains by case analysis of gradients of rail lines, weights of trains, utilization ratios of braking force, speeds of trains at their initial brakes. Countermeasures to improve the braking effect are proposed accordingly. Through computation and result analysis, the influences form the factors are identified, namely, gradients of rail lines, weights of trains, utilization ratios of braking force, speeds of trains at their initial brakes. The study results are able to improve the work of designing urban rail lines, choices of technical parameters of trains, layouts of signals and speed limits of rail lines.