Intellectualization of the urban and rural bus: The arrival time prediction method

被引:3
作者
Wang, Yunna [1 ]
机构
[1] Henan Univ Urban Construct, Sch Architecture & Urban Planning, Dept Urban & Rural Planning, Longxiang Ave, Pingdingshan 467036, Henan, Peoples R China
关键词
urban and rural bus; intelligent bus; arrival time prediction; relative error;
D O I
10.1515/jisys-2021-0017
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
To improve the intelligence of urban and rural buses, it is necessary to realize the accurate prediction of bus arrival time. This paper first introduced urban and rural buses. Then, the arrival time prediction was divided into two parts: road travel time and stop time, and they were predicted by the support vector regression method and k-nearest neighbor (KNN) method. A section of a bus route in Pingdingshan city of Henan province was taken as an example for analysis. The results showed that the method designed in this study had better accuracy, and the result was closer to the actual value, with a maximum error of 84 s, a minimum error of 10 s, an average error of 42.5 s, and an average relative error of 5.74%, which could meet the needs of passengers. The results verify the reliability of the designed method in predicting the arrival time of urban and rural buses, which can be popularized and applied in practice.
引用
收藏
页码:689 / 697
页数:9
相关论文
共 20 条
  • [11] Matyakubov M, 2019, ACTA TURIN POLYTECH, V9, P3
  • [12] Validating the coverage of bus schedules: A Machine Learning approach
    Mendes-Moreira, Joao
    Moreira-Matias, Luis
    Gama, Joao
    de Sousa, Jorge Freire
    [J]. INFORMATION SCIENCES, 2015, 293 : 299 - 313
  • [13] SMARTBUS: A Web Application for Smart Urban Mobility and Transportation
    Ram, Sudha
    Wang, Yun
    Currim, Faiz
    Dong, Fan
    Dantas, Ezequiel
    Saboia, Luiz Alberto
    [J]. PROCEEDINGS OF THE 25TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'16 COMPANION), 2016, : 363 - 368
  • [14] Improvement in direct bus services through route planning
    Suman, Hemant K.
    Bolia, Nomesh B.
    [J]. TRANSPORT POLICY, 2019, 81 : 263 - 274
  • [15] Sun DH, 2018, TRANSPORT RES REC, V2034, P62
  • [16] Bi-level programming model for multi-modal regional bus timetable and vehicle dispatch with stochastic travel time
    Wei, Ming
    Sun, Bo
    [J]. CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 2017, 20 (01): : 401 - 411
  • [17] Determination of rice root density from Vis-NIR spectroscopy by support vector machine regression and spectral variable selection techniques
    Xu, Shengxiang
    Zhao, Yongcun
    Wang, Meiyan
    Shi, Xuezheng
    [J]. CATENA, 2017, 157 : 12 - 23
  • [18] Yang C, 2020, J HARBIN I TECH NEW, V27, P35
  • [19] Zhang H, 2017, J MECH ENG RES DEV, V40, P164
  • [20] Application of Grey Prediction Model to Short-time Passenger Flow Forecast
    Zhang, Zhen
    Xu, Xiao
    Wang, Zhan
    [J]. MATERIALS SCIENCE, ENERGY TECHNOLOGY, AND POWER ENGINEERING I, 2017, 1839