Data-Driven Approaches to Mining Passenger Travel Patterns: “Left-Behinds” in a Congested Urban Rail Transit Network

Chen, Xing; Zhou, Leishan; Bai, Zixi; Yue, Yixiang; Guo, Bin; Zhou, Hanxiao

doi:10.1155/2019/6830450

Cited by 5 publications

(5 citation statements)

References 20 publications

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“…Here 𝜂 is parameterized by the methods in Section 3. The parameter 𝜽 denotes 𝜷 in (7) in logspline density estimation, and 𝜽 = ( 𝜸 ′ , 𝜅 ) ′ in (8) in Gaussian process reconstruction density estimation. It is hard to obtain the MLE of 𝝓 through directly optimizing (11).…”

Section: Estimation For No-transfer Situationsmentioning

confidence: 99%

“…In these studies, arrival and departure time of passengers at various stations and transfer time at various interchange stations are assumed to obey certain probability distributions. The distributions of walking time are used as key parameters in passenger flow simulations (Sun and Schonfeld, 5 Hörcher et al, 6 and Zhu et al 4 ) and left‐behind studies (Zhu et al 7 and Chen et al 8 ). Thus, much attention has been drawn to estimation of parameters in the literature.…”

Section: Introductionmentioning

confidence: 99%

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

Xiong

Sun

et al. 2021

Appl Stoch Models Bus & Ind

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With 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.

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Section: Estimation For No-transfer Situationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Xiong

Sun

et al. 2021

Appl Stoch Models Bus & Ind

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show abstract

“…Observations of tap-out times are generated according to each parameter combination with n � 100 and 200. In the proposed Bayesian method, we use the method in the last paragraph of Section 3 to determine the hyperparameters α in (7). We first compute the estimators 􏽢 τ k , k � 1, .…”

Section: (18)mentioning

confidence: 99%

“…With continuous improvement of rail transit, automatic fare collection (AFC) systems can collect a wealth of passenger travel information and provide an important data support for metro operation departments to study passenger travel characteristics, formulate passenger flow control measures, and adjust driving plans. In recent years, mining behavior characteristics of passengers from AFC data has become an active branch in the field of passenger flow research (see, e.g., Paul [1]; Kusakabe et al [2]; Sun and Xu [3]; Zhao and Yao [4]; Zhou et al [5]; Sun and Schonfeld [6]; Chen et al [7]; and Wu et al [8]). For a fixed pair of origindestination stations, studying behaviors of passengers based on only AFC data is actually a black box problem due to the lack of boarding information, which brings great challenges in accurately describing passenger flows.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Analysis for Metro Passenger Flows Using Automated Data

Xiong

Sun

et al. 2022

Mathematical Problems in Engineering

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With the fast development of metro systems in many big cities, it is important to study the characteristics of passenger flows based on metro data for the management to guarantee service quality and safety. In this article, we build statistical models for the data of passengers’ tap-in and tap-out times in both no-transfer and one-transfer cases, and propose a Bayesian approach to estimate parameters in the models. These estimators can be used to evaluate a number of measures, which describe degrees of congestion and comfort, and to quantify their uncertainties. Application of our approach to Beijing metro shows different passengers follow different patterns between different routes and between off-peak and peak hours.

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“…For example, Zhang [ 18 ] estimated the network-wide link travel time and station waiting time using AFC data in an urban rail transit system. Chen [ 19 ] proposed a methodology to mine passenger travel patterns based on AFC data and automatic vehicle location data. Nevertheless, the passenger trajectories in the subway network are hard to be easily obtained because: (1) passenger travel behavior is affected by individual subjective factors; and (2) the subway network has strong time-dependent characteristics [ 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

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

Zhi

et al. 2022

Entropy

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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.

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Data-Driven Approaches to Mining Passenger Travel Patterns: “Left-Behinds” in a Congested Urban Rail Transit Network

Cited by 5 publications

References 20 publications

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

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

Bayesian Analysis for Metro Passenger Flows Using Automated Data

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

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