|Table of Contents|

[1] Shen Jin, Zhao Jiandong, Gao Yuan, et al. Probabilistic interval prediction of metro-to-bus transferpassenger flow in the trip chain [J]. Journal of Southeast University (English Edition), 2022, 38 (4): 408-417. [doi:10.3969/j.issn.1003-7985.2022.04.010]

Probabilistic interval prediction of metro-to-bus transferpassenger flow in the trip chain()

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

2022 4
Research Field:
Traffic and Transportation Engineering
Publishing date:


Probabilistic interval prediction of metro-to-bus transferpassenger flow in the trip chain
Shen Jin1 Zhao Jiandong1 2 Gao Yuan3 Feng Yingzi1 Jia Bin1
1School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
2Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China
3School of Traffic and Transportation, Northeast Forestry University, Harbin 150040, China
urban traffic probabilistic interval prediction deep learning metro-to-bus transfer passenger flow trip chain
To accurately analyze the fluctuation range of time-varying differences in metro-to-bus transfer passenger flows, the application of a probabilistic interval prediction model is proposed to predict transfer passenger flows. First, bus and metro data are processed and matched by association to construct the basis for public transport trip chain extraction. Second, a reasonable matching threshold method to discriminate the transfer relationship is used to extract the public transport trip chain, and the basic characteristics of the trip based on the trip chain are analyzed to obtain the metro-to-bus transfer passenger flow. Third, to address the problem of low accuracy of point prediction, the DeepAR model is proposed to conduct interval prediction, where the input is the interchange passenger flow, the output is the predicted median and interval of passenger flow, and the prediction scenarios are weekday, non-workday, and weekday morning and evening peaks. Fourth, to reduce the prediction error, a combined particle swarm optimization(PSO)-DeepAR model is constructed using the PSO to optimize the DeepAR model. Finally, data from the Beijing Xizhimen subway station are used for validation, and results show that the PSO-DeepAR model has high prediction accuracy, with a 90% confidence interval coverage of up to 93.6%.


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Biographies: Shen Jin(1997—), female, Ph.D. graduate; Zhao Jiandong(corresponding author), male, doctor, professor, zhaojd@bjtu.edu.cn.
Foundation items: The National Key Research and Development Program of China(No. 2019YFB160-0200), the National Natural Science Foundation of China(No. 71871011, 71890972/71890970).
Citation: Shen Jin, Zhao Jiandong, Gao Yuan, et al. Probabilistic interval prediction of metro-to-bus transfer passenger flow in the trip chain[J].Journal of Southeast University(English Edition), 2022, 38(4):408-417.DOI:10.3969/j.issn.1003-7985.2022.04.010.
Last Update: 2022-12-20