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Vehicle Breakdown Duration Modelling

Wang, W., Chen, H. and Bell, M.C. (2005) Vehicle Breakdown Duration Modelling. Journal of Transportation and Statistics, 8 (1). pp. 75-84. ISSN 1094-8848

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Abstract

This paper analyzes the characteristics of vehicle breakdown duration and the relationship between the duration and vehicle type, time, location, and reporting mechanisms. Two models, one based on fuzzy logic (FL) and the other on artificial neural networks (ANN) were developed to predict the vehicle breakdown duration. One advantage of these methods is that few inputs are needed in the modeling. Moreover, the distribution of the duration does not affect the results of the prediction. Predictions were compared with the actual breakdown durations demonstrating that the ANN model performs better than the FL model. In addition, the paper advocates for a standard way to collect data to improve the accuracy of duration prediction.

Item Type: Article
Copyright, Publisher and Additional Information: This is an author produced version of a paper subsequently published in 'Journal of Transportation and Statistics' -published by the Bureau of Transport Statistics. Added in accordance with the publisher's self-archiving policy.
Keywords: traffic incident management, vehicle breakdown duration, fuzzy logic, neural networks
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds)
Depositing User: Adrian May
Date Deposited: 15 Mar 2007
Last Modified: 04 Aug 2014 20:07
Published Version: http://www.bts.gov/publications/journal_of_transpo...
Status: Published
Publisher: Bureau of Transport Statistics
Refereed: Yes
Related URLs:
URI: http://eprints.whiterose.ac.uk/id/eprint/2013

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