Prediction of Bus Travel Time Over Unstable Intervals between Two Adjacent Bus Stops

Mansur As, Tsunenori Mine, Tsubasa Yamaguchi

研究成果: ジャーナルへの寄稿記事

抜粋

This paper addresses the problem of predicting bus travel time over unstable intervals between two adjacent bus stops using two types of machine learning techniques: ANN and SVR methods. Our model considers the variability of travel time because the travel time is often influenced by stochastic factors, which increase the variance of travel time over an interval between inter-time periods. The factors also affect the variance of the travel time over the interval at the same time period between inter-days. In addition, the factors show some correlation of travel time over the interval between time periods in a day. The performance of the proposed model is validated with real bus probe data collected from November 21st to December 20th, 2013, provided by Nishitetsu Bus Company, Fukuoka, Japan. We demonstrated the impact of two types of input variables for the prediction in off- and on-peak (rush hour) periods. The results show that the two types of inputs can effectively improve the prediction accuracy. Moreover, we compared the proposed method with our previous methods. The experimental results show the validity of our proposed method.

元の言語英語
ページ(範囲)53-64
ページ数12
ジャーナルInternational Journal of Intelligent Transportation Systems Research
18
発行部数1
DOI
出版物ステータス出版済み - 1 1 2020

    フィンガープリント

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Neuroscience(all)
  • Information Systems
  • Automotive Engineering
  • Aerospace Engineering
  • Computer Science Applications
  • Applied Mathematics

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