The ever-increasing travel demand has brought great challenges to the organization, operation, and management of the subway system. An accurate estimation of passenger flow distribution can help subway operators design corresponding operation plans and strategies scientifically. Although some literature has studied the problem of passenger flow distribution by analyzing the passengers’ path choice behaviors based on AFC (automated fare collection) data, few studies focus on the passenger flow distribution while considering the passenger–train matching probability, which is the key problem of passenger flow distribution. Specifically, the existing methods have not been applied to practical large-scale subway networks due to the computational complexity. To fill this research gap, this paper analyzes the relationship between passenger travel behavior and train operation in the space and time dimension and formulates the passenger–train matching probability by using multi-source data including AFC, train timetables, and network topology. Then, a reverse derivation method, which can reduce the scale of possible train combinations for passengers, is proposed to improve the computational efficiency. Simultaneously, an estimation method of passenger flow distribution is presented based on the passenger–train matching probability. Finally, two sets of experiments, including an accuracy verification experiment based on synthetic data and a comparison experiment based on real data from the Beijing subway, are conducted to verify the effectiveness of the proposed method. The calculation results show that the proposed method has a good accuracy and computational efficiency for a large-scale subway network.

/pdf/a-calculation-method-of-passenger-flow-distribution-in-large-sza6o16d.pdf

A Calculation Method of Passenger Flow Distribution in Large-Scale Subway Network Based on Passenger–Train Matching Probability

Understanding passenger route choice behavior under the influence of detailed route information based on smart card data

The behavior of passengers in urban railway stations (i.e., metro stations) is dependent on environmental, cultural, and temporal factors. This affects how escalator infrastructures are used, with some passengers standing on one side of the steps to allow others to walk and, in other cases, others standing or walking on both sides of the steps. In this research, escalator infrastructures were studied to better understand the relationship between different conditions and passenger behaviors through a method based on video cameras, passenger detection techniques, and a simulation framework. The results indicate that passenger behavior could affect escalator infrastructure as it varies significantly depending on the time of day, type of station, local culture, and other environmental factors. The implications of these findings for the planning and design of the infrastructure of railway stations are discussed, and practical recommendations are proposed to improve the efficiency of escalator usage in urban railway stations.

Analyzing Escalator Infrastructures: A Pilot Study in Santiago Metro

In this study, we focus on one of the practically important research problems of coordinated passenger path and space-time trajectory estimation in an urban rail transit network based on multi-source observations. This task was accomplished by developing a modeling framework to integrate frequency- and schedule-based passenger assignment approaches. To utilize the heterogeneous information of multisource observations, we established two groups of mapping relations from observations to the decision variables of different models. Flow-based observations were mapped to the link flow variables in the frequency-based passenger assignment model, and individual-based observations were mapped to the passenger space-time trajectory variables in the schedule-based passenger assignment model. To estimate the consistent internal states of the system between path choice and space-time trajectory, we formulated the coupling path flow constraint, which serves as a bridge between flow-based and individual-based decision variables. A general least-squares estimation framework was developed to integrate the path choice estimation in a frequency-based passenger assignment model and the space-time trajectory estimation in schedule-based passenger assignment with coupling constraints. The integrated estimation problem was solved using a Lagrangian relaxation-based heuristic approach. We demonstrated the advantages and practicality of our proposed model based on a large-scale case of the Beijing Subway Network, which includes 26 lines, 450 stations, and more than 5 million passengers, and revealed the benefits of the proposed methodology and its potential for data-driven decision-making in urban transit management centers. 

A modeling framework to integrate frequency - and schedule-based passenger assignment approaches for coordinated path choice and space-time trajectory estimation based on multi-source observations

Efficient passenger flow assignment is essential for ensuring the safe and stable operation of the urban rail transit (URT). This paper proposes a new passenger flow assignment approach to address this challenge. The approach generates two agents: connected vehicle and unconnected passenger, to simulate vehicle operation and passenger travel for the URT network. It derives the passenger trajectory by the passenger route evolution mechanism to achieve the passenger flow assignment for the whole network. Furthermore, it provides statistics on platform passenger flow when obtaining the overall station passenger flow. Meanwhile, the approach employed Automatic Fare Collection (AFC) data as input to analyze the passenger flow features of the Shanghai Metro. The passenger flow assignment results can reveal the start and end of morning and evening peak hours at a time granularity of seconds. That is, 7:16:20 and 9:33:00 for the morning peak and 16:49:00 and 19:22:50 for the evening peak, respectively. The results also reveal the route choice pattern of passengers. Passengers have preferences for each type of route, in descending order: the shortest route, the minimum number of stations route, and the minimum number of transfer stations route, when they have a choice of these three routes. Moreover, the results can accurately identify large passenger flow stations and provide data support for station passenger flow management strategies. 

A new approach on passenger flow assignment with multi-connected agents

In a congested large-scale subway network, the distribution of passenger flow in space-time dimension is very complex. Accurate estimation of passenger path choice is very important to understand the passenger flow distribution and even improve the operation service level. The availability of automated fare collection (AFC) data, timetable, and network topology data opens up a new opportunity to study this topic based on multisource data. A probability model is proposed in this study to calculate the individual passenger’s path choice with multisource data, in which the impact of the network time-varying state (e.g., path travel time) on passenger path choice is fully considered. First, according to the number and characteristics of OD (origin-destination) candidate paths, the AFC data among special kinds of OD are selected to estimate the distribution of passengers’ walking time and waiting time of each platform. Then, based on the composition of path travel time, its real-time probability distribution is formulated with the distribution of walking time, waiting time, and in-vehicle time as parameters. Finally, a membership function is introduced to evaluate the dependence between passenger’s travel time and the real-time travel time distribution of each candidate path and take the path with the largest membership degree as passenger’s choice. Finally, a case study with Beijing Subway data is applied to verify the effectiveness of the model presented in this study. We have compared and analysed the path calculation results in which the time-varying characteristics of network state are considered or not. The results indicate that a passenger’s path choice behavior is affected by the network time-varying state, and our model can quantify the time-varying state and its impact on passenger path choice.

https://downloads.hindawi.com/journals/complexity/2022/5451017.pdf

Data-Driven Method for Passenger Path Choice Inference in Congested Subway Network

The rise of new technology makes the network structure increasingly complex, and the difficulty of management and maintenance increases exponentially. Situation awareness can evaluate the current situation of the whole network and predict the future change trend. Studying the acquisition implementation means, task scheduling model and hierarchical structure, and relying on the LUA script to customize the data acquisition, storage, transmission, and analysis strategies and rules, can build the communication network situation awareness system. Designing the data driven situation awareness pre-acquisition node and analysis center technology path, can adapt to the changing network needs, and provide important decision-making basis for network planning, operation assurance, problem handling, and early warning.

Research on Data driven Communication Network Situation Awareness System

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Data-Driven Method for Passenger Path Choice Inference in Congested Subway Network

A Calculation Method of Passenger Flow Distribution in Large-Scale Subway Network Based on Passenger–Train Matching Probability

Research on Data driven Communication Network Situation Awareness System