Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/83995
Title: Predicting the duration of non-recurring road incidents by cluster-specific models
Authors: Ghosh, Banishree
Muhammad Tayyab Asif
Dauwels, Justin
Cai, Wentong
Guo, Hongliang
Fastenrath, Ulrich
Keywords: Electric breakdown
Accidents
Issue Date: 2016
Source: Ghosh, B., Muhammad Tayyab Asif, Dauwels, J., Cai, W., Guo, H., & Fastenrath, U. (2016). Predicting the duration of non-recurring road incidents by cluster-specific models. 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), 1522-1527.
Conference: 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)
Abstract: In metropolitan areas, about 50% of traffic delays are caused by non-recurring traffic incidents. Hence, accurate prediction of the duration of such events is critical for traffic management authorities. In this paper, we study the predictability of the duration of traffic incidents by considering various external factors. As incident data is typically sparse, training a large number of models (for instance, model for each road) is not possible. On the other hand, training one model for the entire network may not be a suitable solution, as such a model will be too generalized and consequently unsuitable for many relatively rare scenarios. Therefore, we propose to solve this issue by first grouping incidents through common latent similarities among them and then training data-driven predictors for each group. In our numerical analysis we consider incident data from Singapore and the Netherlands. Our results show that by training cluster-specific models we can reduce the prediction error by 19.41% for incidents in Singapore and by 17.8% for incidents in the Netherlands.
URI: https://hdl.handle.net/10356/83995
http://hdl.handle.net/10220/42471
DOI: 10.1109/ITSC.2016.7795759
Schools: School of Electrical and Electronic Engineering 
School of Computer Science and Engineering 
Interdisciplinary Graduate School (IGS) 
Research Centres: Energy Research Institute @ NTU (ERI@N) 
Rights: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [https://dx.doi.org/10.1109/ITSC.2016.7795759].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers
ERI@N Conference Papers
IGS Conference Papers
SCSE Conference Papers

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