Prediction model of Passenger Waiting Time in High-speed Rail Hub Based on BP Neural Network

被引：3
|
作者：
Yu-Long Pei [1 ]
Kan Zhou [1 ]
Ting Peng [1 ]
机构：
[1] Harbin Inst Technol, Sch Traff Sci & Engn, Harbin 150090, Peoples R China
来源：
MECHATRONICS AND INDUSTRIAL INFORMATICS, PTS 1-4 | 2013年 / 321-324卷
关键词：
Traffic engineering; Waiting time; BP neural network; Prediction; High-speed rail hub; SERVICE;
D O I：
10.4028/www.scientific.net/AMM.321-324.1903
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In order to explore the characteristics of passenger waiting time in high-speed rail hub, this paper analyzed the influencing factors of passenger waiting time, based on the survey of passenger waiting time in high-speed rail hub. And the main influencing factors were screened out using variance analysis. Then the prediction model of passenger waiting time based on BP neural network was established, the parameters of the model were calibrated and the validity was verified. The results show that, travel time in urban area, trip distance, familiarity toward the hub, educational background of passengers, and the type of transportation is the main influencing factor of passenger waiting time in high-speed rail hub, and the average relative error is only 9.2% using the proposed prediction model of passenger waiting time based on BP neural network.
引用
收藏
页码：1903 / 1906
页数：4
相关论文
共 50 条
  • [1] Probability Model of Upstream Passenger Who Select Escalator in High-speed Rail Hub
    Pei, Yu-Long
    Zhou, Kan
    Peng, Ting
    [J]. MANUFACTURING PROCESS AND EQUIPMENT, PTS 1-4, 2013, 694-697 : 3366 - 3369
  • [2] A Weighted Passenger Flow Evolving Model for the High-Speed Rail Network Based on the City Attractiveness
    Wan, Tao
    Jiang, Nan
    Guo, Yan
    [J]. 2014 FOURTH INTERNATIONAL CONFERENCE ON INSTRUMENTATION AND MEASUREMENT, COMPUTER, COMMUNICATION AND CONTROL (IMCCC), 2014, : 784 - 787
  • [3] Prediction and Evaluation of the Comfort of a Passenger in a High-Speed Maglev System Based on a Neural Network Algorithm
    Yin, Long
    Sun, Guoxin
    Li, Jun
    Hu, Jie
    [J]. CICTP 2020: TRANSPORTATION EVOLUTION IMPACTING FUTURE MOBILITY, 2020, : 1080 - 1088
  • [4] Study on the cooperative transfer mode of high-speed rail and aviation based on the passenger flow prediction model
    Guo, Xiao
    [J]. BASIC & CLINICAL PHARMACOLOGY & TOXICOLOGY, 2020, 126 : 26 - 26
  • [5] A Rail Service Life Prediction Model Based on BP Neural Network
    Zhang, Wanqiu
    Li, Qing
    Zhang, Jie
    Li, He
    [J]. SEVENTH INTERNATIONAL CONFERENCE ON TRAFFIC ENGINEERING AND TRANSPORTATION SYSTEM, ICTETS 2023, 2024, 13064
  • [6] The Research of Railway Passenger Flow Prediction Model Based on BP Neural Network
    Wang, Yao
    Zheng, Dan
    Luo, Shimin
    Zhan, Dongming
    Nie, Peng
    [J]. ADVANCED DESIGNS AND RESEARCHES FOR MANUFACTURING, PTS 1-3, 2013, 605-607 : 2366 - +
  • [7] Decreasing the passenger waiting time for an intercity rail network
    Vansteenwegen, P.
    Van Oudheusden, D.
    [J]. TRANSPORTATION RESEARCH PART B-METHODOLOGICAL, 2007, 41 (04) : 478 - 492
  • [8] The Research of High-speed Rail Bridge Construction Deformation Prediction System Based on Genetic Neural Network
    Yu, Cheng-Xue
    Tang, Mian
    Ning, Yu-Cai
    He, Xu-Hui
    [J]. PROCEEDINGS OF THE 2014 INTERNATIONAL CONFERENCE ON MECHANICS AND CIVIL ENGINEERING, 2014, 7 : 589 - +
  • [9] Precise Control of High-Speed BLDCM Based on BP Neural Network
    He, Ying
    Mo, Bo
    Li, Yanghua
    [J]. AUTOMATIC CONTROL AND MECHATRONIC ENGINEERING III, 2014, 615 : 365 - 369
  • [10] Equilibrium optimization model of high-speed rail and airline in multi-modal hub network
    Zhang, Jing
    Chen, Zhiya
    Huang, Shuai
    Guo, Xiao
    Chen, Weiya
    [J]. Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology), 2015, 46 (04): : 1544 - 1552
12345