1887
Volume 20, Issue 1
  • ISSN 1572-0373
  • E-ISSN: 1572-0381

Abstract

Abstract

Customized bus services are conducive to improving urban traffic and environment, and have attracted widespread attention. However, the problems encountered in the new customized bus mode include the large difference between the basis of customized bus passenger flow data analysis and the basis of the traditional bus passenger flow data analysis, and the difficulty in different vehicle scheduling caused by the combination of traditional and customized bus modes. We propose a customized bus passenger flow analysis algorithm and multi-destination customized bus line capacity scheduling algorithm, and display them in an intuitive way. The experimental results show that the algorithm model established in this paper can basically meet the data requirements of operation and management, and can provide decision support for customized bus line planning.

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2019-07-15
2019-10-16
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References

  1. Ceder, Avishai
    (2007) Public transit planning and operation: theory, modelling and practice[M]. 13Oxford, UK: Butterworth-Heinemann, Elsevier. 10.1201/b12853
    https://doi.org/10.1201/b12853
  2. He, Jiarong
    (2016) Research and demonstration application of Guangzhou custom bus operation management platform, Graduate thesis of South China University of Technology.
    [Google Scholar]
  3. Klein, Lawrence A.
    (2001) Sensor Technologies and Data Requirements for ITS[M]. Artech House, Boston.
    [Google Scholar]
  4. Li, Chaochao
    (2011) Research on the response strength of financial time series to public information based on Fuzzy Clustering, Graduate thesis of Kunming University of Science and Technology.
    [Google Scholar]
  5. Li, Wang, Jingfeng Yang,
    (2017) A spatial-temporal estimation model of residual energy for pure electric buses based on traffic performance index, Technical Gazette, 24(6):1803–1811.
    [Google Scholar]
  6. (2017) Dynamic Traffic Congestion Simulation and Dissipation Control Based on Traffic Flow Theory Model and Neural Network Data Calibration Algorithm. Complexity, Article ID 5067145. 10.1155/2017/5067145
    https://doi.org/10.1155/2017/5067145 [Google Scholar]
  7. (2018) Time-Space Relationship Analysis Model on the Bus Driving Characteristics of Different Drivers Based on the Traffic Performance Index System, Technical Gazette, 25(1), 236–244.
    [Google Scholar]
  8. Li, Wei, Zhou Feng, Zhu Wei,
    (2015) Research on large data visualization of passenger flow in rail transit network. Chinese Railways, 2:94–98.
    [Google Scholar]
  9. Mo, Yikui, Su Yongyun
    (2010) Neural networks based real-time transit passenger volume prediction: International Conference on Power Electronics and Intelligent Transportation System, [C]. 10.1109/PEITS.2009.5406782
    https://doi.org/10.1109/PEITS.2009.5406782 [Google Scholar]
  10. Tanaka, Mikio, Sato Norio, Sakuma Yasushi, Mafune Kazutoshl
    (2002) A Study On the Application of Data Mining Technology to the Analysis of Passenger Flow Data[J], Railway Technical Research institute, 16, 11:37–42.
    [Google Scholar]
  11. Turner, Shawn M.
    (2007) Advanced Techniques for Travel Time Data Collection[J]. Transportation Research Record, 1551:51–58. 10.1177/0361198196155100107
    https://doi.org/10.1177/0361198196155100107 [Google Scholar]
  12. Wang, Baiping
    (2012) Passenger flow analysis method based on preferred information and its application. Shanghai Railway Science & Technology, (02):58–92.
    [Google Scholar]
  13. Wang, Wei, Zhou Xiaofei
    (2015) An overview of passenger flow analysis in Urban Rail Transit. Sichuan Cement(11):331.
    [Google Scholar]
  14. Wu, Yubing, Yang Yongchang
    (2001) Analysis of passenger flow in public traffic line: The Forum on the modernization of urban public transport in China in the new century.
    [Google Scholar]
  15. Xu, W., Qin, Y., Huang, H.
    (2004) A new method of railway passenger flow forecasting based on spatio-temporal data mining[M], 402–405.
    [Google Scholar]
  16. Yang, C., Z. Li, R. Cui and B. Xu
    (2004–2016, 2014) Neural Network-based Motion Control of An Under-actuated Wheeled Inverted Pendulum Model, IEEE Transactions on Neural Networks and Learning Systems, vol.25no.11, pp.. 10.1109/TNNLS.2014.2302475
    https://doi.org/10.1109/TNNLS.2014.2302475 [Google Scholar]
  17. Yang, Ran, Wu Bin
    (2010) Short-Term Passenger Flow Forecast of Urban Rail Transit Based on BP Neural Network[M], 4574–4577.
    [Google Scholar]
  18. Yang, C., X. Wang, Z. Li, Y. Li and C.-Y. Su
    (2017) Teleoperation Control based on Combination of Wave Variable and Neural Networks, IEEE Transactions on Systems, Man, and Cybernetics: Systems, in press, doi:  10.1109/TSMC.2016.2615061
    https://doi.org/10.1109/TSMC.2016.2615061 [Google Scholar]
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  • Article Type: Research Article
Keyword(s): customized bus , fuzzy clustering , passenger flow forecast , time series and travel heat map

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