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

Document Type

Article

Publication Date

9-1-2004

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. © 2004 Computer-Aided Civil and Infrastructure Engineering.

Identifier

3242717115 (Scopus)

Publication Title

Computer Aided Civil and Infrastructure Engineering

External Full Text Location

https://doi.org/10.1111/j.1467-8667.2004.00363.x

ISSN

10939687

First Page

364

Last Page

376

Issue

5

Volume

19

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