Beijing Metro System Research Articles

Reducing passenger waiting time in oversaturated metro lines with passenger flow control policy

Oversaturation during peak hours brings about severe challenges for metro operations management in megacities. It can deteriorate passengers’ service level experience and increase the safety risk at congested platforms. This paper focuses on designing an online passenger flow control policy to manage passenger flow in each origin-destination (OD) pair so that the total passenger waiting time during the research horizon can be minimized. Suppose that the OD demand information reveals sequentially over time, we formulate the online passenger flow control problem as stochastic dynamic programming (DP). An efficient online adaptive policy is designed to guide the real-time flow control decisions at each stage. To evaluate the performance of our approach, we exploit the realistic transit data from the Beijing metro system to carry out a series of numerical experiments. The computational results show that our approach can significantly reduce the expected total passenger waiting time as well as alleviate metro station congestion compared with the first-come-first-serve (FCFS) policy. The benefits of our approach are obtained by exploiting the reusable nature of train capacity to transport more passengers during rush hours.

Metro Passenger-Flow Representation via Dynamic Mode Decomposition and Its Application.

Passenger-flow anomaly detection and prediction are essential tasks for intelligent operation of the metro system. Accurate passenger-flow representation is the foundation of them. However, spatiotemporal dependencies, complex dynamic changes, and anomalies of passenger-flow data bring great challenges to data representation. Taking advantage of the time-varying characteristics of data, we propose a novel passenger-flow representation model based on low-rank dynamic mode decomposition (DMD), which also integrates the global low-rank nature and sparsity to explore the spatiotemporal consistency of data and depict abrupt data, respectively. The model can detect anomalies and predict short-term passenger flow conveniently and flexibly. For anomaly detection, we further introduce a strong temporal Toeplitz regularization to characterize the temporal periodic change of data, so as to more accurately detect anomalies. We conduct experiments with smart card transaction data from the Beijing metro system to assess the performance of the model in two use cases. In terms of anomaly detection, the experimental results demonstrate that our method can detect anomalies efficiently, especially for time sequence anomalies. As for short-term prediction, our model is superior to other methods in most cases.

Characteristics of the thermal environment, air quality, and passenger comfort in the underground transfer space of metro stations in Beijing

With the increase in Beijing's metro mileage and passenger numbers, it is becoming especially necessary to investigate the indoor environment and passenger comfort of metro stations. In this study, the transfer spaces in the top seven transfer stations with the largest passenger flow within the Beijing metro system were selected for conducting indoor environment (thermal environment and air quality) measurements and a subjective comfort survey of over 600 passengers. The results showed that the thermal environment and air quality experienced by passengers changed dynamically with the passenger transfer process. Indoor temperature and relative humidity (RH) were affected by not only aboveground weather changes but also station depth, and the indoor temperature in winter was more comfortable than that in summer. The trend in subjective feelings of passengers' thermal comfort was similar to that of the relative warmth index (RWI) evaluation of seven transfer stations during the measurement periods, and passengers' thermal comfort during the summer morning rush hours was not ideal but was slightly hot overall. Although the concentrations of carbon dioxide (CO2) and particulate matter (PM: including PM2.5 and PM10) met the indoor air quality maintenance standards, the percentage of passengers uncomfortable with the air quality exceeded 80% during both seasons, and the centralized tendency in winter was very uncomfortable. Passengers are very concerned about air quality, and their subjective feelings are relatively susceptible to psychological factors. Regarding the overall comfort trend, passengers' subjective feelings of comfort during the morning rush hours in summer and winter were both “slightly uncomfortable”. This paper provides a valuable reference for further research on environmental, health, and energy saving issues in metro stations and presents reliable primary research data for the design and operation management of ventilation and air conditioning systems in metro stations.

A Train Frequency Optimization Model for the Joint Operation With Two Intersecting Metro Lines

With the increase of travel demands in the networked metro system, more and more metro transfer stations are suffering from oversaturated situations, leading to the accumulation of passengers in the transfer corridor and boarding area with potential accident risks. To further improve the transfer efficiency and passenger accumulation safety at transfer stations, the joint operation strategy is adopted. Metro trains are allowed to run across different metro lines and passengers switch other lines without transfer. This paper proposes 1) a meaningful, yet simple, way for defining the running strategy of trains for joint operation with two intersecting metro lines and 2) an optimal model for the train frequency to minimize indicators associated with the passenger service and train capacity. Regulation constraints such as waiting time of through and transfer passengers, number of available trains, load factor, and departure interval are taken into account. Finally, a case study of the joint operation in the Beijing metro system is implemented to demonstrate the performance and effectiveness of the proposed approaches.

Statistical estimation in passenger‐to‐train assignment models based on automated data

AbstractWith the rapid development of metro systems, it has become increasingly important to study phenomena such as passenger flow distribution and passenger boarding behavior. It is difficult for existing methods to accurately describe actual situations and to extend to the whole metro system due to the limitations from parameter uncertainties in their mathematical models. In this article, we propose a passenger‐to‐train assignment model to evaluate the probabilities of individual passengers boarding each feasible train for both no‐transfer and one‐transfer situations. This model can be used to understand passenger flows and crowdedness. The input parameters of the model include the probabilities that the passengers take each train and the probability distribution of egress time, which is the time to walk to the tap‐out fare gate after alighting from the train. We present the likelihood method to estimate these parameters based on data from the automatic fare collection and automatic vehicle location systems. This method can construct several nonparametric density estimates without assuming the parametric form of the distribution of egress time. The EM algorithm is used to compute the maximum likelihood estimates. Simulation results indicate that the proposed estimates perform well. By applying our method to real data in Beijing metro system, we can identify different passenger flow patterns between peak and off‐peak hours.

Estimating left behind patterns in congested metro systems: a Bayesian model

Purpose During rush hours, many passengers find it difficult to board the first train due to the insufficient capacity of metro vehicles, namely, left behind phenomenon. In this paper, a data-driven approach is presented to estimate left-behind patterns using automatic fare collection (AFC) data and train timetable data. Design/methodology/approach First, a data preprocessing method is introduced to obtain the waiting time of passengers at the target station. Second, a hierarchical Bayesian (HB) model is proposed to describe the left behind phenomenon, in which the waiting time is expressed as a Gaussian mixture model. Then a sampling algorithm based on Markov Chain Monte Carlo (MCMC) is developed to estimate the parameters in the model. Third, a case of Beijing metro system is taken as an application of the proposed method. Findings The comparison result shows that the proposed method performs better in estimating left behind patterns than the existing Maximum Likelihood Estimation. Finally, three main reasons for left behind phenomenon are summarized to make relevant strategies for metro managers. Originality/value First, an HB model is constructed to describe the left behind phenomenon in a target station and in the target direction on the basis of AFC data and train timetable data. Second, a MCMC-based sampling method Metropolis–Hasting algorithm is proposed to estimate the model parameters and obtain the quantitative results of left behind patterns. Third, a case of Beijing metro is presented as an application to test the applicability and accuracy of the proposed method.

A Three-Layer Model for Studying Metro Network Dynamics

This paper studies the dynamic performance of subway transportation systems. A three-layer model, consisting of a rail layer, a train layer, and a passenger layer, is proposed to describe the structure and operation of a metro system. Two parameters, namely, time efficiency and maximum load ratio , are proposed to assess the transport efficiency and the train utilization rate, respectively. Case studies of the metro networks in Beijing, Tokyo, and Hong Kong show that the time efficiency decreases nonlinearly with the increase of vehicle resource and passenger demand, whereas maximum load ratio varies in an opposite manner. For the three metro networks under study, the Tokyo metro system has the highest time efficiency when passengers adopt a shortest-path (SP) routing strategy, while the Hong Kong system’s highest time efficiency exceeds the others’ when passengers adopt a minimum-transfer-path (MTP) strategy. In general, the maximum time efficiency is higher when passengers adopt SP routing rather than MTP routing. Moreover, the Beijing metro system has the highest maximum load ratio, regardless of the passengers’ routing behavior. This paper can be applied to a metro network to optimize the train departure interval under a certain passenger entrance rate, with the aim to maximize the time efficiency and maximum load ratio. Our model permits assessment of the operational effectiveness of metro systems, which helps the metro operators to improve the performance and reduce the cost.

An event-based probabilistic model of disruption risk to urban metro networks

Metro systems serve large populations and form extensive networks. Incidents such as signal failure, train failure, and power failure, pose great challenges to the reliable operation of metro systems around the world. For the Beijing metro system, incidents caused 408 disruptions of train services from 2014 to 2018. These incidents are investigated in detail, and a Monte Carlo approach and incident parameter functions are used to generate stochastically simulated incident events. Based on the simulated incidents, combined passenger flow data and an input-output model, we estimate the risk associated with the Beijing metro system in terms of disrupted passenger flows by considering risk propagation in the network, where both direct passenger loss and indirect passenger flow loss considering interchanges between different lines are considered. Lines at high risk are identified, and a sensitivity analysis is performed to investigate the effects of risk mitigation measures. This study provides a generic risk modeling method for urban metro systems and can improve decision making to manage metro system risk.

Model and algorithm of coordinated flow controlling with station-based constraints in a metro system

With the growing urban population and its rapid growth of mobility needs, metro systems often suffer from congestion in peak hours in many mega-cities over the world. This incurs severe travel delays for commuters and safety risks for metro operators. Hence, passenger flow management and control becomes an essential way to reduce station congestion during high-peak hours. This paper investigates the passenger flow control problem with the objective of increasing the number of boarding passengers. Considering the scenario that the destination of each passenger entering the station is unknown, a flow control problem with dynamic and station-based constraints is proposed to dynamically determine the number of passengers boarding each train at each station. Compared with existing flow control strategies, this model can improve the equity for boarding passengers of different OD pairs. The station-based flow control problem is formulated as a complicated nonlinear nonconvex quadratic programming model. To solve the intractable nonlinear programming model, we reformulate it into the dynamic programming formation and develop two efficient heuristic algorithms to solve it. We carry out two sets of numerical experiments, including the small-scale case with synthetic data and the real-world case with the operation data of Beijing metro system, to evaluate the performance of our model and algorithms. Several performance indicators, e.g. average waiting time and Gini coefficient, are presented to verify the efficiency and fairness of proposed model. The numerical results applied to Beijing urban subway network indicate that our approach can reduce the passengers’ waiting time and the line-level Gini coefficient by 5.21% and 23.52% compared with the benchmark flow control strategy with maximum loading and station-based constraints.

An Accurate Collaborative Programming Model of Passenger Flow Control for an Oversaturated Metro Network

To reduce potential accident risks and unnecessary time wasting, passenger flow control measures have been widely adopted for metro lines with oversaturated passenger flow. This paper proposes an integer linear programming (ILP) formulation for the optimal coordination of a passenger-oriented flow control strategy in a metro network during peak hours. The objective function of our formulation minimizes the total passenger waiting time at all stations under the premise of guaranteeing safety inside transfer stations. Our ILP formulation simultaneously considers the time-dependent passenger demand, destinations of the boarding passengers, transfer processes and train loading capacity constraints on lines in a metro network. Based on an upper bound on the number of waiting passengers at each station, a linear dynamic proportional method for assigning destinations for the boarding passengers is derived. Moreover, the method maintains the fast convergence of our ILP formulation and lets the formulation support real-time decision-making. Finally, a real-world instance with operation data of the Beijing metro system is implemented to demonstrate the accurate control ability to passenger flow of the proposed approach. As a result, the formulation obtains a solution with a total passenger waiting time of 57,488 min at transfer stations, which is 6698 min less than the value that is obtained with the previously established passenger flow control scheme.

A Data-Driven Approach for Estimating the Effects of Station Closures in Metro Systems

To estimate the impact of station closures, a novel data-driven approach is developed. First, Symbolic Aggregate Approximation (SAX) is designed to classify stations with an anomaly in passenger flow volume. Secondly, to define the trend anomaly within each segment, the Dynamic Time Warping (DTW) approach is suggested. The method simultaneously considers the mean value and trend information for passenger flow data. To check the efficiency of the proposed model, a case analysis of the Beijing metro system is illustrated. The findings suggest that the proposed model is superior in terms of the calculation of the impact of station closures to other state-of-the-art models.

Performance improvement of energy consumption, passenger time and robustness in metro systems: A multi-objective timetable optimization approach

As an effective transport mode to reduce the impact of urban mobility with great capacity and low pollution, metro systems have received rapid development all over the world in recent years. Since a well-designed timetable can improve the service level of passengers, increase the revenue of operating companies, and reduce the environmental pollution, the issue of timetable design/optimization is usually represented as a multi-objective decision problem. This paper develops a multi-objective integer programming model which integrates the energy consumption, the passenger waiting time, and the robustness by optimizing departure and arrival time of trains at each station. A non-dominated sorting genetic algorithm II (NSGA-II) is designed to find the optimal solution. Finally, a case study of Yizhuang line in Beijing metro system is conducted to illustrate the efficiency and effectiveness of the suggested timetable optimization approach.

Cooperative passenger flow control in an oversaturated metro network with operational risk thresholds

The oversaturated situation is now very common in the metro system of some megacities due to large commuting demands in peak hours, which leads to passenger accumulation on platforms and causes potential accident risks. To improve the transport efficiency and passenger accumulation safety at each station, this paper proposes a cooperative passenger flow control optimization in a specific metro network, in which each passenger can freely switch trains between different metro lines with a single ticket. Through considering the dynamic characteristics and transfer behaviors of passengers, an effective bi-objective integer linear programming model is formulated to characterize the passenger control process, in which the objectives are to minimize the total passenger waiting time and passenger accumulation risks at all the involved stations. To solve the proposed model conveniently, the above model is transformed into a single-objective model through reformulating the risk objective as a threshold-based constraint. Finally, two sets of numerical experiments, including a small-scale case and a real-world instance with operation data of the Beijing metro system, are implemented to demonstrate the performance and effectiveness of the proposed approaches.

Improving Service Quality With the Fuzzy TOPSIS Method: A Case Study of the Beijing Rail Transit System

Rail transit (RT) has been favored by passengers because it effectively alleviates the problems of dense population, housing shortages, small natural areas, and serious air pollution in urban centers. In this paper, we propose a framework that combines statistical analysis, fuzzy theory, and the technique for order preference by similarity to an ideal solution (TOPSIS) to evaluate the service quality of RT. First, the passenger perception of service quality is modeled as trapezoidal fuzzy numbers from the fuzzy theory, which solves the uncertainty problem of passenger perception that how factors affect service quality. Next, a case study that evaluates the service quality of the Beijing metro system is proposed using the fuzzy TOPSIS method. During the evaluation process, 8011 surveys are collected from 16 metro lines operated by Beijing Metro Operating Company Ltd. The evaluation results show that transfers, in-vehicle experience, and ticket purchases or recharges are the three factors that passengers find most unsatisfactory about metro travel and that need to be greatly improved in the future construction and management of metros. Furthermore, we analyze the stableness of the fuzzy TOPSIS method by the ranking change of service quality for a line from different comparison sets of metro lines. Finally, we provide suggestions and guidance for the optimization of RT infrastructure and investment.

Empirical Analysis of Traveling Backwards and Passenger Flows Reassignment on a Metro Network with Automatic Fare Collection (AFC) Data and Train Diagram

Metro (subway) systems are becoming overcrowded in some of China’s mega-cities, such as Beijing, Shanghai, and Guangzhou. As many passengers are unable to board trains on an overcrowded metro network during peak periods, some of them are willing to spend more time and energy by traveling backwards in order to secure a seat or even room for standing. Traditional studies on travel behavior analysis and transit assignment models seldom deal with this situation. We propose a methodology including the affinity propagation cluster method with between-within-proportion (BWP) index and an adaptive “0-1” model named the traveling backwards model (TBM) to identify the phenomenon of “traveling backwards” and to reassign passenger flows on a metro network using automatic fare collection (AFC) data and actual train diagrams. As a numerical example, this integrated approach is applied to the Beijing metro system. Our research shows that the affinity propagation cluster method with BWP index and implicit enumeration algorithm for TBM work well. TBM is a good replacement for the existing assignment model and travel behavior analysis, especially for those mega-cities’ networks in peak hours.

Modeling departure time choice of metro passengers with a smart corrected mixed logit model - A case study in Beijing

It is critical to improve the effectiveness of demand management in metro systems with passenger departure time choice exactly learned during peak hours. In this study, a practical framework is developed to model departure time choice of metro passengers during peak hours. First, various attributes that influence departure time choice of metro passengers are investigated and the technique for order preference by similarity to ideal solutions (TOPSIS) is used to identify these main attributes. Then, a mixed logit (ML) model of departure time choice that accounts for price endogeneity is developed. To calibrate the model, a stated preference (SP) survey based on D-efficient design is conducted in the Beijing metro system. It is proved that the corrected ML model outperforms the uncorrected ML model according to the collected 1152 sample data. An elasticity analysis of these main attributes is further conducted, which indicates that metro fare and departure time change influence passenger departure time choice more than crowdedness in Beijing metro. Knowledge of these preferences assists traffic managers in balancing passenger departure time to mitigate overcrowding during peak hours. Heterogeneity of passenger socioeconomic and trip characteristics is also concerned taking advantage of ML model. Finally, a ML-based fare discount strategy to ease the crowdedness in Batong Line of Beijing metro is presented and evaluated via an existing simulation tool.

Commuter health risk and the protective effect of three typical metro environmental control systems in Beijing, China

The Beijing metro system consists of eighteen lines, and approximately 10 million commuters take the metro daily. Despite the presence of three different environmental control systems, the metro’s air quality has caused widespread concern. This study investigated the particulate matter (PM) concentrations in the metro system, and assessed the protective effect of the three environmental control systems. The study evaluated the ratio of PM2.5 to PM10 to assess the relative proportion of PM2.5 and to investigate the influence of PMx on commuter health risk, using settlement theory and a comprehensive indoor air-quality index (CIAI), respectively. The average PM2.5 and PM10 concentrations on different system ranged from 25.4 to 145.5 (PM2.5) and from 26.2 to 165.0 μg/m3 (PM10). Moreover, the values of PM2.5/PM10 are greater than 83.5%; the PM2.5/PM10 ratio in the air was lower on the platform than inside the metro carriage. PM2.5 and PM10 concentrations are significantly correlated. PM concentrations inside carriages and on the platforms increased when the doors were open and when the metro approached. The CIAI values of PM2.5 were at levels classified as “unhealthy for sensitive groups,” exposing children and people with disabilities to health risks. The variation rate of PM2.5 and PM10 levels (1.94 and 6.24%) was lower when platform screen doors were present. Metro system air quality on the platform can be improved by installing platform screen doors to prevent dirty air from the tunnel from entering the platform and adjusting ventilation system fan speeds based on outdoor air quality.

Service-oriented train timetabling with collaborative passenger flow control on an oversaturated metro line: An integer linear optimization approach

With the drastic increase of travel demands in urban areas, more and more metro lines are nowadays suffering from oversaturated situations, leading to the accumulation of passengers on platforms with potential accident risks. To further improve the service quality and reduce accident risks, this paper proposes an effective method for collaboratively optimizing the train timetable and accurate passenger flow control strategies on an oversaturated metro line. Through considering the dynamic characteristics of passenger flow, a rigorous integrated integer linear programming model is firstly formulated to minimize the total passenger waiting time at all of involved stations, in which the train timetable provides a service-oriented operation plan and optimal passenger flow control is imposed to avoid congestion on platforms within the transportation capacities. To solve the problem of interest efficiently, a hybrid algorithm, which combines an improved local search and CPLEX solver, is designed to search for high-quality solutions. Finally, two sets of numerical experiments, including a small-scale case and a real-world instance with operation data of the Beijing metro system, are implemented to demonstrate the performance and effectiveness of the proposed approaches.

Impact evaluation of a mass transit fare change on demand and revenue utilizing smart card data

Transit fares are an effective tool for demand management. Transit agencies can raise revenue or relieve overcrowding via fare increases, but they are always confronted with the possibility of heavy ridership losses. Therefore, the outcome of fare changes should be evaluated before implementation. In this work, a methodology was formulated based on elasticity and exhaustive transit card data, and a network approach was proposed to assess the influence of distance-based fare increases on ridership and revenue. The approach was applied to a fare change plan for Beijing Metro. The price elasticities of demand for Beijing Metro at various fare levels and trip distances were tabulated from a stated preference survey. Trip data recorded by an automatic fare collection system was used alongside the topology of the Beijing Metro system to calculate the shortest path lengths between all station pairs, the origin–destination matrix, and trip lengths. Finally, three fare increase alternatives (high, medium, and low) were evaluated in terms of their impact on ridership and revenue. The results demonstrated that smart card data have great potential with regard to fare change evaluation. According to smart card data for a large transit network, the statistical frequency of trip lengths is more highly concentrated than that of the shortest path length. Moreover, the majority of the total trips have a length of around 15km, and these are the most sensitive to fare increases. Specific attention should be paid to this characteristic when developing fare change plans to manage demand or raise revenue.

An Incentive Scheme for Equipment Replacement Subsidy in Beijing Metro System

The Beijing metro system is owned by a state-owned company on behalf of the government who fully financed its construction. The service provision and equipment replacement are delegated to semi-private or state-owned operating companies. To meet the public affordability, the fare in Beijing metro system is heavily regulated by the government. The fare income however does not cover the cost of operating company. Government subsidy is therefore required to ensure the sustainable operation of Beijing metro. Cost-plus and fixed-price contracts are commonly used subsidy contract, but they lack incentives to reduce the amount of government subsidy. This paper proposes introduction of incentives to the contract to reduce the amount of government subsidy while the resulting equipment reliability does not compromise service quality and safety. In the proposed contract, the government first pays an agreed sum and then shares the deviation of the final cost from the agreed price with the operating company. An illustrative study demonstrates that the proposed incentive scheme is able to encourage the operating companies to reduce their costs and thus the government subsidy while the level of service safety and quality are upheld.