Network-Scale Passenger Flow Forecasting Methods in URT Based on Similarity Measurement
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 2
Abstract
Accurate passenger flow forecasting in urban rail transit (URT) could provide a vital reference for operators’ timely operation management. However, due to the enormous scale of the metro network, it is unwise to forecast passenger flows at the station scale individually. In this paper, a forecasting framework is proposed for network-scale forecasting tasks considering both accuracy and efficiency. There are mainly three stages in the forecasting framework. Firstly, three kinds of similarity measurements are presented regarding the adjacent similarity, geographic location similarity, and trend similarity. Secondly, three similarity graphs are formed by combining the three kinds of similarity measurements and flow time series. Thirdly, the multigraph network is applied to perform passenger flow forecasting. The experimental results indicated that the proposed method performs relatively accurately for the network-scale prediction with economic time costs. Specifically, the proposed model could acquire the feasible forecasts compared with the best disaggregate model using less than half of calculation costs, and above 10% reduction in root-mean squared error (RMSE) compared with the best aggregate model in the benchmark trials. Extensive contrast experiments were conducted to investigate the sensitivity and interpretability of the proposed model.
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Data Availability Statement
The data, models, or codes generated or used during the study are available from the corresponding author by request.
Acknowledgments
This research has been supported by the Key Program of Sichuan Science and Technology Department (2020YJ0255), which provided funding for this research.
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 149 • Issue 2 • February 2023
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Mar 23, 2022
Accepted: Sep 30, 2022
Published online: Nov 26, 2022
Published in print: Feb 1, 2023
Discussion open until: Apr 26, 2023
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Cited by
- Qiuchi Xue, Wei Zhang, Meiling Ding, Xin Yang, Jianjun Wu, Ziyou Gao, Passenger flow forecasting approaches for urban rail transit: a survey, International Journal of General Systems, 10.1080/03081079.2023.2231133, 52, 8, (919-947), (2023).