Passenger Flow Control Research Articles

Temporal and Spatial Evolution of Passenger Flow in an Urban Rail Transit Network During Station Closure

In this study, we focused on the evolution of passenger flow in an urban rail transit network in both temporal and spatial dimensions under the event of station closure. We constructed an extended space-time-state hyper network so that we could utilize a better-defined three-dimensional solution space to describe passenger behaviors and pedestrian characters. The space-time-state network was extended by adding dummy arcs and setting the “block space-time domain” based on the original spatiotemporal network. Based on the extended space-time-state hyper network, a passenger flow evolution model that aimed to minimize the usage cost during station closure was established considering the strict capacity constraints (including corridor passing capacity, platform load capacity and train transport capacity). For a large-scale urban rail network, we developed a decomposition solution framework in which the Lagrangian relaxation and semi-assignment algorithms were adopted. A real-world instance was implemented based on the Beijing subway network with complete smart card data for each passenger for his/her origin and destination, while the specific space-time trajectories of all passengers and time-dependent passenger volumes in trains and transfer corridors were estimated. The results of a numerical experiment suggest that the proposed passenger flow evolution method can provide a rich set of passenger volume inferences for advanced transit planning and management applications, such as passenger flow control, passenger flow guidance, and real- time train scheduling.

Dynamic Origin-Destination Matrix Estimation Based on Urban Rail Transit AFC Data: Deep Optimization Framework with Forward Passing and Backpropagation Techniques

At present, the existing dynamic OD estimation methods in an urban rail transit network still need to be improved in the factors of the time-dependent characteristics of the system and the estimation accuracy of the results. This study focuses on predicting the dynamic OD demand for a time of period in the future for an urban rail transit system. We propose a nonlinear programming model to predict the dynamic OD matrix based on historic automatic fare collection (AFC) data. This model assigns the passenger flow to the hierarchical flow network, which can be calibrated by backpropagation of the first-order gradients and reassignment of the passenger flow with the updated weights between different layers. The proposed model can predict the time-varying OD matrix, the number of passengers departing at each time, and the travel time spent by passengers, of which the results are shown in the case study. Finally, the results indicate that the proposed model can effectively obtain a relatively accurate estimation result. The proposed model can integrate more traffic characteristics than traditional methods and provides an effective and hierarchical passenger flow estimation framework. This study can provide a rich set of passenger demand for advanced transit planning and management applications, for instance, passenger flow control, adaptive travel demand management, and real-time train scheduling.

Access to the Rail Station as a Customer Value: Simulation of Passenger Flows in Rail Stations with Disinfection Gateway Installations

This research applied secondary data collection methods to predict passenger flows, which involved analyzing traffic flows at rail stations and statistical methods, which involved assessing the relationship between variables and regression. Observation methods were used to measure current passenger flows at the rail station entrances. Passengers’ safety perception was assessed to understand the customer value of transport services. This assessment was based on an online survey, an analysis of official requests from passengers to the station directorates, and feedback from passengers and station visitors posted on the Internet. Traditional and content analysis methods were used to study passengers’ requests and feedbacks. It was substantiated that due to the COVID-19 pandemic, the concept of transportation security and the role of health safety as a customer value in passenger service have become more prominent. The possibility for passenger flow simulation to ensure people’s safe passage through a disinfection gateway is clearly shown. Public space management structures in transportation hubs can use the simulation results of this paper to solve the problem of passenger flow control along with installation of disinfection gateways at the station entrances for additional security. This is one of the few studies that explores the impact of using disinfection gateways to manage passenger flows in train stations.

Adaptive Multilevel Collaborative Passenger Flow Control in Peak Hours for a Subway Line

Due to contradiction of large-scale passenger demand and limited transportation capacity, the passengers who cannot be transported away in time accumulate and congest in stations. To ensure travel safety, improve travel efficiency, and ameliorate waiting environments for passengers, this paper proposes an adaptive multilevel collaborative passenger flow control strategy integrating the control of station entrance and station hall. An integer linear programming model is constructed, which aims at minimizing the total passenger waiting time and taking the safe capacity of each key area of all stations as the necessary constraints. The model is applied in two scenarios with different scales of passenger demand in the morning peak of the Batong line. The results show that the proposed model can adaptively activate the appropriate control level, limit the amount of accumulated passengers in each key area of the station within its safe capacity, and shorten the total passenger waiting time.

Equity-Oriented Train Timetabling with Collaborative Passenger Flow Control: A Spatial Rebalance of Service on an Oversaturated Urban Rail Transit Line

On an oversaturated urban rail transit line, passengers at downstream stations have to wait for more trains until they get aboard, resulting in service imbalance problem. To improve the service quality, this paper proposes an integrated optimization approach combining the train timetabling and collaborative passenger flow control, with the aim of minimizing indicators associated with the passenger service imbalance and train loading capacity utilization. Considering train regulation constraints and passenger loading dynamics, a mixed-integer linear programming model is formulated. Based on the linear weighting technique, an iterative heuristic algorithm combining the tabu search and Gurobi solver is designed to solve the proposed model. Finally, a simple case with different-scale instances is used to verify that the proposed algorithm can obtain near-optimal solution efficiently. Moreover, a real-world case of Beijing Subway Batong Line is implemented to compare performances of the proposed approach with those under the original timetable and noncollaborative passenger flow control.

Research on Passenger Flow Control Model of Urban Rail Transit Network

The current urban rail transit faces the pressure of effective transportation management brought about by the large passenger flow. Through this research, it is proposed to construct a coordinated control model for urban rail transit passenger flow and optimize the urban rail transit operation organization. Using the Mixed Logit model to construct the urban rail transit network passenger flow collaborative control optimization model can accurately describe the distribution of the urban rail transit network passenger flow online, which is especially applicable to the network topology change scenario brought by the new line introduction. Empirical evidence shows the correctness and effectiveness of the model.

Data-driven approach for solving the route choice problem with traveling backward behavior in congested metro systems

This study proposes a data-driven approach to understand traveling backward (TB) behavior while making a route choice in congested metros. First, TB behavior during route choice in overcrowded metros is defined and analyzed. Second, a hybrid model comprising three hierarchical Bayesian models is developed to describe the TB behavior. Third, a novel sampling method based on Hamiltonian dynamics is introduced to estimate the model parameters. Finally, a case study of Beijing metro is discussed to illustrate the effectiveness and robustness of the proposed model. The contributions of application of the proposed model to accurate demand assignment and passenger flow control for metro managers are demonstrated.

Research on Evaluating the Sustainable Operation of Rail Transit System Based on QFD and Fuzzy Clustering.

The purpose of this study is to evaluate the sustainable operation of rail transit system. In rail transit system, as the most important aspect of negative entropy flow, the effective strategy can offset the increasing entropy of the system and make it have the characteristics of dissipative structure, so as to realize the sustainable operation. At first, this study constructs the Pressure-State-Response (PSR) model to evaluate the sustainable operation of rail transit system. In this PSR model, “pressure” is viewed as customer requirements, which answers the reasons for such changes in rail transit system; “state” refers to the state and environment of system activities, which can be described as the challenges of coping with system pressure; “response” describes the system’s actions to address the challenges posed by customer needs, namely operational strategies. Moreover, then, 13 pressure indices, five state indices and 11 response indices are summarized. In addition, based on quality function deployment (QFD), with 13 pressure indices as input variables, five state indices as customer requirements (CRs) of QFD and 11 response indices as technical attributes (TAs) of QFD, this study proposed the three-phase evaluation method of the sustainable operation of rail transit system to obtain the operational strategy (that is, negative entropy flow): The first phase is to verify that 13 pressure indices can be clustered into five state indices by fuzzy clustering analysis; The second phase is to get the weights of five state indices by evidential reasoning; The third phase is to rate the importance of 11 response indices by integrating fuzzy weighted average and expected value operator. Finally, the proposed model and method of evaluation are applied to the empirical analysis of Shanghai rail transit system. Finally, we come to the conclusion that Shanghai rail transit system should take priority from the following five aspects: “advancement of design standards”, “reliability of subway facilities”, “completeness of operational rules”, “standardization of management operation” and “rationality of passenger flow control”.

Analysis of the departure time choices of metro passengers during peak hours

In this study, a stated preference (SP) survey based on a D-efficient experimental design was conducted for the Nanjing Metro to examine the passenger flow and elaborate the heterogeneity of the departure time choice behaviour of metro passengers during peak hours. A latent class logit model was applied to calibrate the data, and the attribute variables were studied using an elasticity analysis. The survey identified four classes of metro passengers in peak hours who have different preferences for the departure time choice. The results show that fixed working hours and arrival time constraints are the major attributes that determine the classification of the passengers, while travel costs and overcrowding are the key factors that affect the departure time choice. However, passengers are more sensitive to metro fares than crowdedness. By reducing travel costs and mitigating overcrowding, passengers’ choice of departure time, especially when deciding whether to depart earlier than usual, was significantly affected. This study aims to provide theoretical support and a basis for decision-making for metro operation departments to enact passenger flow control strategies.

Passenger flow control strategies for urban rail transit networks

Urban rail traffic congestion is becoming increasingly serious due to the large traffic demands in modern cities. In order to ensure the safety and quality of station services in peak hours, it's necessary to adopt some reasonable and effective passenger flow control strategies. In this study, through considering the time-dependent passenger demands, a passenger flow control model based on the network-level system is explicitly developed. The passenger successive motion process is discretized by the modeling method. Systematically considering the coordinated relationship between traffic demands and strict capacity constraints (including station passing capacity, platform load capacity and train transport capacity), we establish a mixed integer linear programming model to minimize the total passenger waiting time (including passengers outside stations and on the platforms). The optimization software Cplex is adopted to solve the developed model, and a real network of Beijing urban railway is calibrated to verify the effectiveness of the suggested model. As a result, the proposed flow control strategies can provide detailed information about control stations, control durations and control intensities, and can effectively reduce the total waiting time and relieve the number of stranded passengers in the urban rail transit network.

Collaborative passenger flow control on an oversaturated metro line: a path choice approach

Focusing on reducing the traffic congestion on an oversaturated urban metro system, this study investigates the collaborative passenger flow control problem for a metro line. By introducing the timetable-oriented space-time network representation, the problem of interest is finally formulated as an integer programming model, in which the objective function aims to minimize the total weighted passenger waiting time at different stations. Since the final decision is closely related to the space-time path-finding process, the proposed model can also be regarded as a coordinated path-finding process with train loading capacity constraints. Then, an efficient heuristic algorithm is developed to solve the proposed model. Finally, a series of numerical examples are implemented on a sample metro line and the Beijing Batong metro line. The computational results show that the proposed approach can produce high-quality passenger flow control strategies for the metro system, thereby demonstrating the effectiveness of the proposed model and algorithm.

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.

Short-term passenger flow prediction under passenger flow control using a dynamic radial basis function network

Short-term passenger flow prediction and passenger flow control are essential for managing congestion in metros. This paper proposes a new dynamic radial basis function (RBF) neural network to forecast outbound passenger volumes and improve passenger flow control. First, we adopt a train timetable to model passenger flow propagation and identify potential stations that substantially impact the outbound volumes of the target stations. As a result, we incorporate inbound volume, outbound volume, and the train timetable to avoid overfitting. Second, passenger flow control is considered to improve the prediction accuracy by adding passenger flow control coefficients to our model, which then attempts to specify the true influence of these potential stations during crowded times. Finally, we construct a dynamic input radial basis function neural network whose performance is illustrated by the following three scenarios: large passenger flow under passenger flow control during morning peak hours, evening peak hours under passenger flow control, and normal passenger flow without passenger flow control. Compared with the backpropagation neural network, the wavelet support vector machine and the K-nearest neighbor, the proposed method achieves the best prediction performance at a half-hour prediction time lag. The proposed method can also identify crucial stations and time periods 30 min in advance, which contributes when considering proactive passenger flow control to alleviate congestion during peak hours in metro networks.

Application of Artificial Intelligence Technology in Metro Passenger Flow Control

With the development of science and technology in China, artificial intelligence technology has been widely applied in many fields because of its advantages, and it also plays a good role in promoting the development of urban subway industry. This paper expounds the technology of artificial intelligence, analyzes the composition of artificial intelligence in urban subway traffic management system, and explores the artificial intelligence technology in urban subway traffic management, in the form of realizing the use of passenger flow control value, inspiring people to constantly explore the application of artificial intelligence technology, and better services in the development of the economy, to improve people’s lives.

Modeling on Coordinated Passenger Flow Control for Multiple Stations in a Single Line of Urban Rail Transit

This paper studies the problem of morning peak passenger congestion in urban rail transit system. Taking multiple stations of a single line as the research object, taking the attributes of train stop and station traffic limit as decision variables, and taking the maximum number of passengers in the urban rail transit system and the minimum proportion variance of each station as the objective function, taking multiple stations of a single line as the research object. A multi-station coordinated passenger flow control model of urban rail transit was constructed and solved by genetic algorithm. In the end, the downward direction of morning peak of Changchun metro line 1 is taken as a case to verify the effectiveness of the model and algorithm.

Integrating Lagrangian and Eulerian observations for passenger flow state estimation in an urban rail transit network: A space-time-state hyper network-based assignment approach

In this study, we focus on one of practically important research problems of integrating Lagrangian and Eulerian observations for passenger flow state estimation in an urban rail transit network. The task is accomplished by using a triple of flow, density, and speed to construct a discretized passenger flow state, further constructing a space-time-state (STS) hyper network so that we can utilize a better defined three-dimensional solution space to integrate structurally heterogeneous data sources. The monitoring data include passenger transaction records and identification space-time samples observed over a possible range of a few hours from origins to destinations (Lagrangian observations), and time-dependent passenger counts collected at some key bottleneck locations (Eulerian observations). To describe the complex urban-rail passenger flow evolution, passenger traveling and fixed sensor state transition processes can be unified within a STS path representation. To estimate the consistent system internal states between two different types of observations, we formulate a hyper network-based flow assignment model in a generalized least squares estimation framework. For applications in large-scale transportation networks, we decompose the proposed model into three easy-to-solve sub-problems. The proposed model is applied to a real-world case based on the Beijing subway network with complete smart card data for each passenger at his/her origin and destination and time-dependent passenger counts in several key transfer corridors, while the specific space-time trajectories of all passengers and high-resolution time-dependent congestion levels at platforms, in trains, and in transfer corridors are estimated. This proposed passenger flow state inference method can provide a rich set of state inferences for advanced transit planning and management applications, for instance, passenger flow control, adaptive travel demand management, and real-time train scheduling.

Collaborative optimization for metro train scheduling and train connections combined with passenger flow control strategy

For high-frequency metro lines, the excessive travel demand during the peak hours brings a high risk to metro system and a low comfort to passengers, so it is important to consider passenger flow control when designing the metro train scheduling strategy. This paper presents a collaborative optimization method for metro train scheduling and train connections combined with passenger control strategy on a bi-directional metro line. Specifically, the dynamic equations for the train headway and train passenger loads along the metro line, the turnaround operations and the entering/exiting depot operations are considered simultaneously. The proposed collaborative optimization problem is formulated as a mixed integer nonlinear programming model to realise the trade-off among the utilization of trains, passenger flow control strategy and the number of awaiting passengers at platforms, which is further reformulated into mixed integer linear programming (MILP) model. To handle the complexity of this MILP model, a Lagrangian relaxation-based approach is designed to decompose the original problem into two small subproblems, which reduces the computational burden of the original problem and can efficiently find a good solution of the train schedule and train connections problem combined with passenger flow control strategy. The numerical experiments are implemented to investigate the effectiveness of the proposed model and approach, which shows that the proposed model is not sensitive to uncertain passenger demand. Under the proposed collaborative optimization approach, the number of train service connections and the crowding inside stations and carriages with the proper passenger flow control strategy can be evidently balanced, and thereby the operation efficiency and safety of the metro lines are effectively improved.

A Passenger Flow Control Method for Subway Network Based on Network Controllability

The volume of passenger flow in urban rail transit network operation continues to increase. Effective measures of passenger flow control can greatly alleviate the pressure of transportation and ensure the safe operation of urban rail transit systems. The controllability of an urban rail transit passenger flow network determines the equilibrium state of passenger flow density in time and space. First, a passenger flow network model of urban rail transit and an evaluation index of the alternative set of flow control stations are proposed. Then, the controllable determination model of the urban rail transit passenger flow network is formed by converting the passenger flow distribution into a system state equation based on system control theory. The optimization method of passenger flow control stations is established via driver node matching to realize the optimized control of network stations. Finally, a real-world case study of the Beijing subway network is presented to demonstrate that the passenger flow network is controllable when driver nodes compose 25.3% of the entire network. The optimization of the flow control station, set during the morning peak, proves the efficiency and validity of the proposed model and algorithm.

Critical subway stations identification for passenger flow control by applying network controllability

ABSTRACTPassenger flow control on a limited number of stations is a common way to maintain the service of a subway system at a desired level. Critical subway stations identification is the key for controlling a complex subway system. Based on network controllability analysis, this paper proposes a passenger flow network (PFN) for a subway system to capture the state dependencies among stations, and develops the analysis procedure for solving the station selection problem. Practical analysis is conducted using data from the Shanghai subway system. The results show that the critical station identifications are dependent on the time period for control. The stations connected with few stations, or with low passenger volume may also be critical for subway system performance, especially stations with low inflow. This study contributes a quantitative method for subway system managers to develop effective passenger flow control strategies.

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.