Probabilistic interval prediction of metro-to-bus transfer passenger flow in the trip chain

作     者：Shen Jin Zhao Jiandong Gao Yuan Feng Yingzi Jia Bin 申瑾;赵建东;高远;冯迎紫;贾斌

作者机构：School of Traffic and TransportationBeijing Jiaotong UniversityBeijing 100044China Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive TransportBeijing Jiaotong UniversityBeijing 100044China School of Traffic and TransportationNortheast Forestry UniversityHarbin 150040China 

出 版 物：《Journal of Southeast University(English Edition)》 (东南大学学报（英文版）)

年 卷 期：2022年第38卷第4期

页      面：408-417页

核心收录：

学科分类：08[工学] 082303[工学-交通运输规划与管理] 082302[工学-交通信息工程及控制] 0823[工学-交通运输工程] 

基　　金：The National Key Research and Development Program of China(No.2019YFB160-0200) the National Natural Science Foundation of China(No.71871011,71890972/71890970) 

主　　题：urban traffic probabilistic interval prediction deep learning metro-to-bus transfer passenger flow trip chain 

摘      要：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 ***,bus and metro data are processed and matched by association to construct the basis for public transport trip chain ***,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 ***,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 ***,to reduce the prediction error,a combined particle swarm optimization(PSO)-DeepAR model is constructed using the PSO to optimize the DeepAR ***,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%.