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Article published In:
Human Robot Collaborative Intelligence: Theory and applications
Edited by Chenguang Yang, Xiaofeng Liu, Junpei Zhong and Angelo Cangelosi
[Interaction Studies 20:1] 2019
► pp. 42–60

Passenger flow forecast for customized bus based on time series fuzzy clustering algorithm

| South China Agricultural University
| Guangdong Lingnan Vocational and Technical College
| Shenyang Institute of Automation Guangzhou Chinese Academy of Sciences | Shenyang Institute of Automation Chinese Academy of Science
| Guangzhou Customs District P.R. China laboratory
| Guangzhou Customs District P.R. China laboratory
| North China Electric Power University
| Guangzhou Yuntu Information Technology Co, Ltd. | Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography

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.

Keywords: customized bus, fuzzy clustering, time series, passenger flow forecast, travel heat map
Article outline
  • 1.Introduction
  • 2.Customized bus passenger flow forecasting algorithm
  • 3.Method of constructing travel-demand heat map
  • 4.Test and analysis
    • 4.1Forecast analysis of travel passenger flow
    • 4.2Expression method of travel-demand heat map
  • 5.Conclusions
  • Acknowledgments
  • References
Published online: 15 July 2019
DOI logo
https://doi.org/10.1075/is.18040.li
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