Urban Rail Transit System Research Articles

CrowdAtlas: Estimating Crowd Distribution within the Urban Rail Transit System

While urban rail transit systems are playing an increasingly important role in meeting the transportation demands of people, precise awareness of how the human crowd is distributed within such a system is highly necessary, which serves a range of important applications including emergency response, transit recommendation, and commercial valuation. Most rail transit systems are closed systems where once entered the passengers are free to move around all stations and are difficult to track. In this article, we attempt to estimate the crowd distribution based only on the tap-in and tap-out records of all the rail riders. Specifically, we study Singapore MRT (Mass Rapid Transit) as a vehicle and leverage EZ-Link transit card records to estimate the crowd distribution. Guided by a key observation that the passenger inflows and arrival flows at different MRT stations and time are spatio-temporally correlated due to behavioral consistency of MRT riders, we design and implement a machine learning-based solution, CrowdAtlas, that captures MRT riders’ transition probabilities among stations and across time, and based on that accurately estimates the crowd distribution within the MRT system. Our comprehensive performance evaluations with both trace-driven studies and real-world experiments in MRT disruption cases demonstrate the effectiveness of CrowdAtlas.

Noise contribution analysis of a U-shaped girder bridge with consideration of frequency dependent stiffness of rail fasteners

The U-shaped girder has been widely constructed in urban rail transit systems, and related investigations have been conducted to study the vibration and noise radiation. However, the constant stiffness fastener (CSF) model was used in most previous research when simulating the noise radiation of U-shaped girder bridges. Moreover, the sound radiation from piers was generally ignored in noise prediction. In this study, a numerical model of U-shaped girder bridge with piers and frequency dependent stiffness fasteners (FDSF) was developed. Then, the frequency domain method was used to simulate both the dynamic response of the bridge and the associated noise radiation. Field tests were also conducted to verify the accuracy of the proposed method. Finally, the acoustic contribution and sound pressure distribution of the pier and girder were discussed based on the FDSF model. The research shows the simulated dynamic responses agree well with the experimental results in the frequency range of interest (20 to 200 Hz). The predicted noise by using FDSF model is closer to measured results than the case using constant fastener stiffness, which indicates that the frequency dependence of rail fasteners should be considered in noise simulation. The piers have obvious influence on the vibration and noise radiation of the bridge structure, especially at the support pier section, where the noise level is larger than that at the mid span. The structure-borne noise from the pier is distributed in a narrow frequency band (50 to 80 Hz), and only affects the acoustic field in the limited space around the pier. In contrast, the sound contribution of the girder noise to the total noise level is dominant in most of the area.

Demand-driven integrated train timetabling and rolling stock scheduling on urban rail transit line

This paper aims to simultaneously optimise train timetabling and rolling stock scheduling in a bidirectional urban rail transit line. A novel variable train composition strategy is adopted to respond to time-dependent passenger demand by allowing modular train units to split and couple with each other at depots. We mathematically model this problem in an arrival time revision framework, simplifying the problem structure and rendering the model solved by a commercial solver. In particular, the proposed model considers two realistic first-in-first-out rules to capture passenger transportation accurately in the oversaturated urban rail transit system. The efficiency of the proposed strategy and model is validated on an illustrative example and a real-world instance in the Yizhuang Line of Beijing Subway. Results show that adopting the variable train composition strategy can save 63.6% and 44.6% of total passenger waiting time compared to the current fixed train composition strategy with eight and six units.

Train Control and Schedule Integrated Optimization With Reversible Substations

The Carbon Neutralization is a grand strategy to deal with global climate change. As a major energy consumer, urban rail transit (URT) system should contribute corresponding solutions. This paper proposes an energy-efficient train control and schedule integrated optimization method under DC traction power supply system (TPSS) with reversible substation (RSS). The dynamic coupling mechanism between the train movement process and the RSS working mode is analyzed and modeled. Aiming to minimize the total RSS energy consumption for the train traveling over multi-interstation with a given total trip time, the train trajectory and schedule optimization problem is integrated in the same level, and is formulated as a multi-phase optimal control problem (MOCP) to be solved by a pseudo-spectral method (PSM). As a supplement, a customized dynamic programming (DP) method for the problem is introduced to verify the validity of the proposed solution framework. Simulation results under the models of the RSS energy together with conventional rectifier substation energy and vehicle mechanical energy are presented and compared, indicating that the proposed methodology has great energy saving performance for the new-type DC railway system.

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.

Performance of urban rail transit: a review of measures and interdependencies

ABSTRACT Recent years saw immense growth in performance measurement literature related to public transit systems, with a clear segmentation between financial and quality-of-service performance frameworks. Recently, there has been a shift away from considering cost efficiency alone as a performance measure, and quality-of-service – which influences ridership attraction and retention – has been receiving more interest. The segmentation of these two performance aspects poses a gap in the literature, as there are interdependencies between them. This study provides a systematic review of the methodologies and empirical findings of studies on both performance measurement aspects of urban rail transit systems; specifically, we demonstrate the importance of linking cost efficiency analyses to the level of service quality. To our knowledge, this is the first review of urban rail transit research that links the two performance aspects. We begin by reviewing the methodological limitations of cost performance measures and summarising the drivers of cost performance in the existing literature. We then review studies on the definitions and measurements of quality-of-service in urban rail performance. Lastly, we summarise the scant literature linking the two performance aspects and highlight future study directions, mainly, the importance of a structural framework to provide a holistic view of transit operators’ performance.

Learning Spatial-Temporal Dynamics for Short-Term Passenger Flow Prediction in Urban Rail Transit

Accurate short-term passenger flow prediction in urban rail transit (URT) plays an important role in ensuring the stable operation of the URT systems. Because of the complex dynamic spatial-temporal dependencies and potential semantic correlations of the URT network, accurate and effective short-term passenger flow prediction is challenging. To solve these problems, a novel model called the dynamic spatial-temporal graph convolutional network (DSTGCN) was proposed. Firstly, spatial semantic graphs (SSGs) were established to encode the spatial dependencies and semantic correlations of the URT network. Meanwhile, the dynamic graph convolutional network (DGCN) with the spatial attention mechanism was used to learn the dynamic spatial correlations of the nodes in the SSGs. Then, the long short-term memory (LSTM) network was integrated into the DGCN to learn the dynamic changes of passenger flow and capture local temporal dependencies. Moreover, the temporal attention mechanism was introduced after LSTM to capture global dynamic temporal correlations by adjusting the weights of different sequence information. Finally, the full connection layers were used to output the prediction results. Several experiments were conducted on Nanning Metro Line 1 real datasets to evaluate the model. The experimental results showed that the DSTGCN can effectively capture the dynamic spatial-temporal dependencies and semantic associations of the passenger flow. Besides, the prediction performances of the DSTGCN were better than those of existing baseline models, and it can provide technical support for improving the intelligent planning and operation decisions of URT systems.

Analysis of the Abnormality of Traction Energy Consumption in Urban Rail Transit System

Traction energy consumption (TEC) is a critical part of the total energy consumption in urban rail transit (URT) systems. Energy consumption patterns and abnormal analysis of TEC guarantee the energy‐saving URT operation. With the rapid development of urbanization, the current energy consumption is becoming more and more prominent with some inherent drawbacks, such as complex original data, complicated statistical analysis, and abnormal energy consumption. This paper proposes a method for time accrual abnormal analysis of TEC. The system architecture of TEC typical values is presented, composed of three elements: research object, evaluation index, and time scale. The time series prediction algorithm calculates the typical values of the cumulative energy consumption index in each energy consumption mode. For the abnormality in TEC mode, the distance of the string vector is used as the similarity measure. Then, the similarity‐based anomaly analysis method is used to judge the pattern abnormality. By comparing the advantages and disadvantages of engineering practice and theoretical research methods, we analyze the applicability of traditional anomaly detection algorithms to perform anomaly analysis of TEC in URT systems. The adopted time accrual abnormal analysis achieves a high fault detection rate, outperforming other models.

Modeling and Simulation Analysis for Urban Rail Transit Hub

Urban rail transit has become the main mode to ease traffic congestion, and urban rail transit hub is the most important area for passenger flow distribution in urban rail transit system. Study on the method of simulation and dynamic evaluation of urban rail transit hub has an important significance for train scheduling, operation organization and risk prevention under the viewpoint of traffic safety and service level. This paper analyzed the system characteristics of urban rail transit hub, proposed agent-based modeling and simulation design, simulation process based on Anylogic, dynamic evaluation index set and classification criteria. Furthermore, Beijing South Subway Station was selected for case study, simulation models which included hub simulation model, passenger flow simulation model and train simulation model were built, and experiment results showed that simulation models had a good performance, which was accordant with the reality scene, simulation error was 0.027. Meanwhile, dynamic evaluation which included distribution efficiency, dynamic service level and emergency capacity during the operation period was researched, and optimizing suggestion was proposed, which could provide the beneficial reference for traffic managers.

Integrated Development of Urban Rail Transit and Energy Systems Supported by Underground Space

Integrated Development of Urban Rail Transit and Energy Systems Supported by Underground Space

Connectivity of metro station location with urban space – a study of Hanoi metro line N°2.3

The shift toward public transport has become essential for sustainable urban development; enhancing the convenience and comfort of users. As a modern urban public transportation, the metro/urban rail rapid system has many advantages such as mass transit with large capacity, fast speed, green and energy saving. Improving the connection of metro stations is the key to attracting passengers to achieve sustainable development. To meet the demand for the strategy to evolve in line with its socio-economic development, Hanoi has promoted the urban rail transit system, and the comprehensive development of stations will change urban citizens' travel lifestyles. This paper presents connectivity space around the metro station location in metro lines in Hanoi – a study of Metro line 2.3

Evaluating skip-stop policy in urban rail transit systems based on passenger cost

Increasing the operating speed in public transport systems can increase the system capacity, reduce the overall passenger travel time and improve experienced comfort. Skip-stop operation, where subsets of the trains operating on the same tracks skip certain intermediate stops, can accelerate the service and improve passengers’ overall travel experience. This paper considers the problem of deciding whether skip-stop operation is beneficial for a given line and which stopping scheme is the most effective. In particular, we investigate whether a simple decision rule for determining the stopping pattern under a skip-stop strategy, derived from the expected weighted time benefits to the passengers, can reliably determine the most suitable skip-stop scheme. To evaluate the impact of alternative stop-skipping strategies, we adopt the existing public transit assignment model BusMezzo, which allows for a realistic representation of passengers’ experienced waiting and in-vehicle travel times and the resulting trade-offs between passenger costs and benefits. The decision rule is applied to a set of high-frequency urban rail lines in Stockholm, Sweden. We show that a simple decision rule may not be a robust way of determining a beneficial skip-stop scheme. The results from the case study reveal that the skip-stop operation can have an overall positive impact on passenger generalized travel time but only under certain conditions at the stops along the line.

Energy-Saving Optimization Method of Urban Rail Transit Based on Improved Differential Evolution Algorithm

The transformation of railway infrastructure and traction equipment is an ideal way to realize energy savings of urban rail transit trains. However, upgrading railway infrastructure and traction equipment is a high investment and difficult process. To produce energy-savings in the urban rail transit system without changing the existing infrastructure, we propose an energy-saving optimization method by optimizing the traction curve of the train. Firstly, after analyzing the relationship between the idle distance and running energy-savings, an optimization method of traction energy-savings based on the combination of the inertia motion and energy optimization is established by taking the maximum idle distance as the objective; and the maximum allowable running speed, passenger comfort, train timetable, maximum allowable acceleration and kinematics equation as constraints. Secondly, a solution method based on the combination of the adaptive dynamic multimodal differential evolution algorithm and the Q learning algorithm is applied to solve the optimization model of energy-savings. Finally, numeric experiments are conducted to verify the proposed method. Extensive experiments demonstrate the effectiveness of the proposed method. The results show that the method has significant energy-saving properties, saving energy by about 11.2%.

Identification of the critical accident causative factors in the urban rail transit system by complex network theory

Considering the complexity of the urban rail transit system (URTS), it is necessary to systematically identify the critical factors and their relationships to avoid or prevent urban rail transit accidents. In response to the problems few previous studies have modeled networks for the whole system components of URTS and have focused too much on the identification of specific component states or behaviors without quantitative analysis of inter-component relationships. This paper proposed a risk network construction and analysis method based on complex network theory to identify the critical accident causative factors and their relationships of the URTS system. Firstly, the accident is defined as the combination of a sequence of risk incidents occurring in a certain order and their resulting consequences, and the definitions of risk point and risk incident were given. Secondly, a risk network construction and analysis method based on the combination of accident reports and complex network theory was proposed to obtain the critical factors and their relationships in the process of the accident. At last, this paper constructed the urban rail transit risk point set containing 39 risk points, built the risk network based on 201 accident reports of URTS in China and analyzed the critical risk points and risk relationships. The results show that this study can provide a new perspective for identifying the critical causative factors of URTS accidents and their relationships for practical application in risk analysis and accident prevention.

Modeling of Hyper-Viscoelastic Properties of High-Damping Rubber Materials during the Cyclic Tension and Compression Process in the Vertical Direction.

With the rapid development of the economy and urbanization, the construction of the urban rail transit system has had a great impact on the work, life, and health of residents in buildings along the rail transit line. Thus, it is particularly urgent and necessary to develop base isolation technologies to control and reduce the impact of vibrations of rail transit systems on building structures. High-damping rubber isolation bearings have shown significant effectiveness in the reduction of this impact, and their isolation performance mainly depends on the mechanical and damping energy dissipation characteristics of the high-damping rubber material. This paper aims to investigate the hyper-viscoelastic properties of the high-damping rubber material used for high-damping rubber isolation bearings during the cyclic tension and compression process in the vertical direction. These properties include hyperelastic parameters, viscoelastic coefficients, and the relaxation times of the material. For this purpose, uniaxial cyclic tension and compression tests were conducted. A three-element Maxwell rheological model combining a strain energy density function was proposed for modeling the hyper-viscoelastic behaviors of the materials during the cyclic tension and compression process. Based on the obtained results, an iterative identification procedure was used to determine the constitutive parameters of the material for each loading-unloading cycle. The aforementioned parameters were further expressed as a function of the number of cycles. New insights into hyper-viscoelastic property changes in this high-damping rubber material during the cyclic tension and compression process were gained in this work. These investigations could facilitate the development of computational tools, which would regulate fundamental guidelines for the better controlling and optimization of the isolation performance of the high-damping rubber material used for high-damping rubber isolation bearings, which have a wider perspective of applications in the urban rail transit system.

Predicting the Impact of Disruptions to Urban Rail Transit Systems

Service disruptions of rail transit systems have become more frequent in the past decade in urban cities, due to various reasons, such as power failures, signal errors, and so on. Smart transit cards provide detailed tapping records of commuters, which enable us to infer their trajectories under both normal and disruptive circumstances. In this article, we study and predict the impact of disruptions on commuters and further evaluate the vulnerability of the rail system. Specifically, we define two metrics, stay ratio and travel delay, to quantify the impact, and we derive the predictor of each metric based on the inferred alternative route choices of commuters under disruptive circumstances. We demonstrate that the alternative route choices contribute to more similar feature distribution among different disruptions, which is crucial to tackling the main challenge of abnormal data scarcity and is beneficial for obtaining more reliable predictors for future disruptions. We evaluate our approach with a real-world transit card dataset. The result demonstrates the effectiveness of our method. Based on the predictors, we further analyse the vulnerability of the rail system. An evaluation with cross validation from taxi GPS trajectory data indicates its efficacy in discovering vulnerable rail stations as well as Origin-Destination pairs.

CEEMDAN-IPSO-LSTM: A Novel Model for Short-Term Passenger Flow Prediction in Urban Rail Transit Systems.

Urban rail transit (URT) is a key mode of public transport, which serves for greatest user demand. Short-term passenger flow prediction aims to improve management validity and avoid extravagance of public transport resources. In order to anticipate passenger flow for URT, managing nonlinearity, correlation, and periodicity of data series in a single model is difficult. This paper offers a short-term passenger flow prediction combination model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and long-short term memory neural network (LSTM) in order to more accurately anticipate the short-period passenger flow of URT. In the meantime, the hyperparameters of LSTM were calculated using the improved particle swarm optimization (IPSO). First, CEEMDAN-IPSO-LSTM model performed the CEEMDAN decomposition of passenger flow data and obtained uncoupled intrinsic mode functions and a residual sequence after removing noisy data. Second, we built a CEEMDAN-IPSO-LSTM passenger flow prediction model for each decomposed component and extracted prediction values. Third, the experimental results showed that compared with the single LSTM model, CEEMDAN-IPSO-LSTM model reduced by 40 persons/35 persons, 44 persons/35 persons, 37 persons/31 persons, and 46.89%/35.1% in SD, RMSE, MAE, and MAPE, and increase by 2.32%/3.63% and 2.19%/1.67% in R and R2, respectively. This model can reduce the risks of public health security due to excessive crowding of passengers (especially in the period of COVID-19), as well as reduce the negative impact on the environment through the optimization of traffic flows, and develop low-carbon transportation.

Resilience Assessment of an Urban Rail Transit Network Under Short-Term Operational Disturbances

Operational disturbances within 30 min, namely, short-term operational disturbances (STODs), occur frequently during the daily operation of an urban rail transit (URT) system. Therefore, there is an urgent need to assess a network’s ability to respond to STODs to improve its operational level. The resilience of a URT network jointly considering turn-back operations, the occurrence time of STODs, and passenger travel experience is addressed. By considering passenger travel alternatives under STODs, we assessed the resilience of a network by utilizing the ratio of the average loss of a time-dependent performance indicator, which is referred to as the operational service level indicator in this paper. A simulation-based resilience assessment flowchart is also proposed. Numerical experiments conducted on the Chengdu subway network indicate that this network is more resilient to successive station failure than to simultaneous failure. Guaranteeing the normal operation of transfer stations is vital for enhancing the resilience of a network. The metric proposed in this paper is more practical for assessing resilience than the two commonly used metrics (performance loss and disturbance duration). Additionally, the experimental results highlight that turn-back operations at the stations have a significant impact on the resilience of a URT network. The sensitivity analysis of the parameters indicates that ensuring the normal operation of links connected by critical stations, preventing large-scale station failure, improving passenger tolerance to disturbance durations, and restoring the connectivity of disrupted links rapidly have practical significance for enhancing the resilience of a network.

Simulation of energy-efficient operation for metro trains: A discrete event-driven method based on multi-agent theory

Along with the rapid development of urban rail transit systems, the increase in operating mileage is the direct result of the enormous energy consumption. Among them, the energy consumption of train traction operation is close to half of the total energy consumption, leading to a gradual increase in operation cost. Based on the discrete event-driven method of multi-agent simulation, this paper simulates the process of the train operation with four function modules. Ingenuity, trains with onboard equipment would store braking regenerative energy and then transmit regenerative energy to other traction trains in the same power supply section. Moreover, the evaluation method of renewable energy utilization efficiency also is proposed with different operation schemes to calculate train operation energy consumption. Further, an adaptive large neighborhood search algorithm is designed to improve computational efficiency for real-time applications. Lastly, the operation of the Beijing Metro Yizhuang line is utilized as a case study to evaluate the simulation model and algorithm. The results show that the simulation method can reasonably simulate the process of train operation according to the actual situation. By optimizing the train operation time and dwell time of different onboard power storage equipment scenarios, the traction energy consumption can be reduced by 3.95% and up to 17.71%, which has a remarkable effect on energy saving.

Land Premium Effects of Urban Rail Transit and the Associated Policy Insights for TOD: A Case of Ningbo, China

With considerable investments, mainly from local government budgets, the construction and operation of urban rail transit (URT) can exert significant spillover effects on the surrounding land use and land prices. In particular, China’s local governments are actively committed to developing their URT systems and promoting large-scale transit-oriented development (TOD) projects under the public land leasing policy. However, the connection between the land premium effects and TOD policy and practice is still lacking, particularly in the local government contexts, which exhibit significant policy and spatial heterogeneity. Thus, this research represents an attempt to better address this issue using the city of Ningbo as a case study. First, the premium effects of URT on land prices are examined, after which three crucial policy insights (land value capture [LVC], public–private cooperation [PPC], and urban regeneration) are proposed to enhance the effectiveness and efficiency of TOD, demonstrating its strong connection with the potential premium effects. The findings demonstrate that (1) local governments have adopted different innovative policies—with the ambition—to implement LVC; (2) assisted by PPC, the local rail transit authority can significantly amplify the premium effects, although it must still address the fair distribution of premiums across multiple stakeholders; and (3) transit-oriented urban regeneration can significantly influence land prices/land rents and subsequently generate significant gentrification, which will be further addressed by the TOD policy and practice.