A dynamic bus-arrival time prediction model based on APC data

Mei Chen, Xiaobo Liu, Jingxin Xia, Steven I. Chien

Research output: Contribution to journalArticlepeer-review

144 Scopus citations

Abstract

Automatic passenger counter (APC) systems have been implemented in various public transit systems to obtain bus occupancy along with other information such as location, travel time, etc. Such information has great potential as input data for a variety of applications including performance evaluation, operations management, and service planning. In this study, a dynamic model for predicting bus-arrival times is developed using data collected by a real-world APC system. The model consists of two major elements: the first one is an artificial neural network model for predicting bus travel time between time points for a trip occurring at given time-of-day, day-of-week, and weather condition; the second one is a Kalman filter-based dynamic algorithm to adjust the arrival-time prediction using up-to-the-minute bus location information. Test runs show that this model is quite powerful in modeling variations in bus-arrival times along the service route.

Original languageEnglish (US)
Pages (from-to)364-376
Number of pages13
JournalComputer-Aided Civil and Infrastructure Engineering
Volume19
Issue number5
DOIs
StatePublished - Sep 1 2004

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design
  • Computational Theory and Mathematics

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