| Peer-Reviewed

Application of Support Vector Machine in Bus Travel Time Prediction

Received: 28 June 2018     Accepted: 12 July 2018     Published: 1 August 2018
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Abstract

The travel time between bus stops has obvious characteristics of time interval distribution, and the bus is a typical space-time process object, and its operation has a state transition. In order to predict the travel time between bus stations accurately, a support vector machine (SVM) algorithm is proposed based on the measured travel time between bus stations. Through a large number of GPS data in different periods of time for a reasonable classification summary bin selected the appropriate kernel function to verify. The algorithm is verified by the actual operation data of No. 6 bus in Qingdao Economic and technological Development Zone. The results show that the results of support vector machine model operation are basically in agreement with the actual measured data, and the accuracy is relatively high, and it can even be used to predict bus travel time.

Published in International Journal of Systems Engineering (Volume 2, Issue 1)
DOI 10.11648/j.ijse.20180201.15
Page(s) 21-25
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2018. Published by Science Publishing Group

Keywords

Public Transport, Bus Travel Time Prediction, Support Vector Machine, Machine Learning

References
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[4] J. Peng Xinjian, Weng Xiaoxiong. Bus travel time prediction based on firefly algorithm optimized BP neural network. Journal of Guangxi Normal University, 2015 (07), pp. 28-36.
[5] J. Xiao Yang, Gao Xiaoli, Li Ting. Intelligent bus data system design. Information technology and information technology, 2015 (07), pp. 51-52.
[6] C. Ma Z L, Ferreira L, Mesbah M. Modelling Bus Traval Tine Reliability Using Supply and Demand Data from AUTOMATIC Vehicle Location and Smart Card Systems. Transportation Research Board 94th Annual Meeting, 2015 (15-0402)
[7] D. Yin Tingting. Research on Bus Dispatching Rules Based on Big Data. Beijing: School of Transportation and Transportation, Beijing Jiaotong University, 2015.
[8] J. Liu Siwen. Thinking about public transportation big data. Urban public transportation, 2015 (9), pp. 21-23.
[9] M. Ran Bin, Chen Xianghui, Zhang Jian. General Theory and Practice of Wisdom Highway. Beijing: China Communications Press, 2015, 23-26.
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[11] J. Kumar B A, Mothukuri S, Vanajakshi L. Analytical approach to identify the optimum inputs for a bus travel time prediction method. Transportation Research Record. Journal of the Transportation Research Board, 2015, 2535, pp. 25-34.
[12] J. Ibarra Rojas O J, Delgado F, Giesen R. Planning, operation, and control of bus transport systems: A literature review. Transportation Research Part B Methodological, 2015, pp. 38-75.
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Cite This Article
  • APA Style

    Zhang Junyou, Wang Fanyu, Wang Shufeng. (2018). Application of Support Vector Machine in Bus Travel Time Prediction. International Journal of Systems Engineering, 2(1), 21-25. https://doi.org/10.11648/j.ijse.20180201.15

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    ACS Style

    Zhang Junyou; Wang Fanyu; Wang Shufeng. Application of Support Vector Machine in Bus Travel Time Prediction. Int. J. Syst. Eng. 2018, 2(1), 21-25. doi: 10.11648/j.ijse.20180201.15

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    AMA Style

    Zhang Junyou, Wang Fanyu, Wang Shufeng. Application of Support Vector Machine in Bus Travel Time Prediction. Int J Syst Eng. 2018;2(1):21-25. doi: 10.11648/j.ijse.20180201.15

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  • @article{10.11648/j.ijse.20180201.15,
      author = {Zhang Junyou and Wang Fanyu and Wang Shufeng},
      title = {Application of Support Vector Machine in Bus Travel Time Prediction},
      journal = {International Journal of Systems Engineering},
      volume = {2},
      number = {1},
      pages = {21-25},
      doi = {10.11648/j.ijse.20180201.15},
      url = {https://doi.org/10.11648/j.ijse.20180201.15},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijse.20180201.15},
      abstract = {The travel time between bus stops has obvious characteristics of time interval distribution, and the bus is a typical space-time process object, and its operation has a state transition. In order to predict the travel time between bus stations accurately, a support vector machine (SVM) algorithm is proposed based on the measured travel time between bus stations. Through a large number of GPS data in different periods of time for a reasonable classification summary bin selected the appropriate kernel function to verify. The algorithm is verified by the actual operation data of No. 6 bus in Qingdao Economic and technological Development Zone. The results show that the results of support vector machine model operation are basically in agreement with the actual measured data, and the accuracy is relatively high, and it can even be used to predict bus travel time.},
     year = {2018}
    }
    

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  • TY  - JOUR
    T1  - Application of Support Vector Machine in Bus Travel Time Prediction
    AU  - Zhang Junyou
    AU  - Wang Fanyu
    AU  - Wang Shufeng
    Y1  - 2018/08/01
    PY  - 2018
    N1  - https://doi.org/10.11648/j.ijse.20180201.15
    DO  - 10.11648/j.ijse.20180201.15
    T2  - International Journal of Systems Engineering
    JF  - International Journal of Systems Engineering
    JO  - International Journal of Systems Engineering
    SP  - 21
    EP  - 25
    PB  - Science Publishing Group
    SN  - 2640-4230
    UR  - https://doi.org/10.11648/j.ijse.20180201.15
    AB  - The travel time between bus stops has obvious characteristics of time interval distribution, and the bus is a typical space-time process object, and its operation has a state transition. In order to predict the travel time between bus stations accurately, a support vector machine (SVM) algorithm is proposed based on the measured travel time between bus stations. Through a large number of GPS data in different periods of time for a reasonable classification summary bin selected the appropriate kernel function to verify. The algorithm is verified by the actual operation data of No. 6 bus in Qingdao Economic and technological Development Zone. The results show that the results of support vector machine model operation are basically in agreement with the actual measured data, and the accuracy is relatively high, and it can even be used to predict bus travel time.
    VL  - 2
    IS  - 1
    ER  - 

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Author Information
  • Traffic College, Shandong University of Science and Technology, Qingdao, China

  • Traffic College, Shandong University of Science and Technology, Qingdao, China

  • Traffic College, Shandong University of Science and Technology, Qingdao, China

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