Prediction of Bus Travel Time over Intervals between Pairs of Adjacent Bus Stops Using City Bus Probe Data

Takuya Kawatani, Tsubasa Yamaguchi, Yuta Sato, Ryotaro Maita, Tsunenori Mine

Research output: Contribution to journalArticlepeer-review

Abstract

Prediction of bus travel time is a crucial tool for passengers. We present methods to predict bus travel time over intervals between pairs of adjacent bus stops using city bus probe data. We apply Gradient Boosting Decision Trees to several kinds of features extracted from the probe data. Experimental results illustrate that adding a combination of features improves the accuracy of travel time prediction over the target interval. In particular, the method using a combination of the travel time over the interval previous to the target one and the number of stops the bus makes before reaching the target interval has better performance than the other methods which use all the other combinations of four features used in this study.

Original languageEnglish
Pages (from-to)456-467
Number of pages12
JournalInternational Journal of Intelligent Transportation Systems Research
Volume19
Issue number2
DOIs
Publication statusPublished - Jun 2021

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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