Estimation of Travel Time Variability Using Bus Probe Data

Mansur As, Tsunenori Mine, Hiroyuki Nakamura

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Prediction of bus travel times is of crucial importance for passengers in letting them know their departure time from an origin and arrival time at a destination and allowing them to make decisions (e.g., postpone departure time at certain hours) and to reduce their waiting time at bus stops. To predict bus travel times, it is important to know whether the target routes are stable or not. In this paper, we propose a time series approach to predict the travel time over an interval between two adjacent bus stops. We build Artificial Neural Network (ANN) models to predict the travel time over the interval. To make accurate predictions, we divide a day into 8 time-periods in calculating travel time over the interval and classify unstable intervals into three types: weak, medium and strong unstable. We use bus probe data collected from November 21st to December 20th 2013 and provided by Nishitetsu Bus Company, Fukuoka, Japan. Experimental results show that our models can effectively improve the prediction accuracy of travel times over intervals by focusing on the three unstable classes and calculating travel times for each interval at each of 8 time-periods in a day.

Original languageEnglish
Title of host publication6th IEEE International Conference on Advanced Logistics and Transport, ICALT 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages199-204
Number of pages6
ISBN (Electronic)9781538616239
DOIs
Publication statusPublished - Nov 26 2018
Event6th IEEE International Conference on Advanced Logistics and Transport, ICALT 2017 - Bali, Indonesia
Duration: Jul 24 2017Jul 27 2017

Publication series

Name6th IEEE International Conference on Advanced Logistics and Transport, ICALT 2017 - Proceedings

Other

Other6th IEEE International Conference on Advanced Logistics and Transport, ICALT 2017
CountryIndonesia
CityBali
Period7/24/177/27/17

All Science Journal Classification (ASJC) codes

  • Management Science and Operations Research
  • Automotive Engineering
  • Control and Optimization
  • Transportation

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