A Method of Generating Effective Paths of Urban Rail Transit Based on Passenger Fare Collection Data

YIN Shisong; LU Bincheng; YE Mao; YANG Zhiqiang

doi:10.3963/j.jssn.1674-4861.2023.02.009

Volume 41 Issue 2

Apr. 2023

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Article Navigation > Journal of Transport Information and Safety > 2023 > 41(2): 86-94

YIN Shisong, LU Bincheng, YE Mao, YANG Zhiqiang. A Method of Generating Effective Paths of Urban Rail Transit Based on Passenger Fare Collection Data[J]. Journal of Transport Information and Safety, 2023, 41(2): 86-94. doi: 10.3963/j.jssn.1674-4861.2023.02.009

Citation:

YIN Shisong, LU Bincheng, YE Mao, YANG Zhiqiang. A Method of Generating Effective Paths of Urban Rail Transit Based on Passenger Fare Collection Data[J]. Journal of Transport Information and Safety, 2023, 41(2): 86-94. doi: 10.3963/j.jssn.1674-4861.2023.02.009

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A Method of Generating Effective Paths of Urban Rail Transit Based on Passenger Fare Collection Data

doi: 10.3963/j.jssn.1674-4861.2023.02.009

YIN Shisong^{1, 2
,},
LU Bincheng^{1, 2},
YE Mao^{1, 2
,
,},
YANG Zhiqiang³

1.
School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China
2.
MIIT Key Lab of Traffic Information Fusion & System Control, Nanjing University of Science and Technology, Nanjing 210094, China
3.
Guangzhou Metro Group Co., Ltd, Guangzhou 510000, China

Received Date: 2022-06-23
Available Online: 2023-06-19

Abstract

Abstract

It is a fundamental task to generate effective paths for predicting cross-section passenger flows, calculat-ing network capacity, and analyzing passenger demand of urban rail transit system. To solve the problems of tradi-tional effective path generation in which the validity of each path cannot be evaluated, and the linear constraint can-not be assigned, as well as aiming at reducing the influences of randomness of questionnaire on the final path set generation, a method of generating effective path sets is developed based on traditional survey data. The proposed method analyzes route choice behavior of passengers and makes corresponding hypothesis. Then, models for evalu-ating effective routes under different clusters are established by introducing passenger trip duration. Additionally, the stations and the lines between them are abstracted as nodes and edges of the network of rail transit. And then, considering route types, subjective factors, and the density of passenger flows, the passenger's travel time data are processed using the adaptive DBSCAN algorithm and are divided into clusters according to the density of passenger flows at different time intervals. Furthermore, taking the clustering results as the input, a Logit model is developed to replace the linear constraints in path generation. Weights of effective paths represented by clusters are calculated separately, and the effective path set is obtained based on its continuity characteristics at travel time intervals. Exper-iments are conducted based on multiple origin-destination trip data from the metro network in the City of Guang-zhou. Study results show that the effective paths have an average adjusted Rand index of 0.652. Compared with the traditional algorithms, the adjusted Rand index has improved by 0.379. It indicates that the proposed method produc-es a smoother set boundary in the plane of route length and transfer time, which is more adaptable to complex and changeable networks.
- Urban rail transit,
- Effective path,
- Travel data,
- Density-based clustering,
- K-short circuit algorithm

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References(21)

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