NDLI: A comparative study of k-NN and hazard-based models for incident duration prediction

Content Provider	IEEE Xplore Digital Library
Author	Araghi, B.N. Hu, S. Krishnan, R. Bell, M. Ochieng, W.
Copyright Year	2014
Description	Author affiliation: Dept. of Civil & Environ. Eng., Imperial Coll. London, London, UK (Hu, S.; Krishnan, R.; Ochieng, W.) \|\| BlipSystems, Vodskov, Denmark (Araghi, B.N.) \|\| Inst. of Transp. & Logistics, Univ. of Sydney, Sydney, NSW, Australia (Bell, M.)
Abstract	The motivation behind this paper is to enhance the reliability of in-vehicle navigation systems by predicting the duration of incidents that cause congestion. The main objective of this paper is to develop a methodology for predicting incident duration using broadcast incident data and evaluate the performance of k-NN and hazard-based duration models for predicting incident duration; both of the models are presented in this paper. An incident dataset from the BBC for the Greater London area is used to evaluate the accuracy of both models so that the results give a direct comparison between the models. The strengths and weaknesses of the models are discussed in the paper based on this analysis. Results show that both k-NN and hazard based models have the potential to provide accurate incident duration prediction. While k-NN based models provided marginally more accurate prediction than hazard-based models, the hazard-based duration models can provide additional information such as delay probabilities that can be used by advanced routing and navigation algorithms. Results also show that traffic information incident feeds, such as the tpegML feed from the BBC or TMC information, can be used as a potential data source for incident duration prediction in vehicle navigation systems.
Starting Page	1608
Ending Page	1613
File Size	473145
Page Count	6
File Format	PDF
ISBN	9781479960781
DOI	10.1109/ITSC.2014.6957923
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-08
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Predictive models Accuracy Hazards Vectors Data models Estimation Navigation
Content Type	Text
Resource Type	Article

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