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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/24155

Title: Real-time classification of aggregated traffic conditions using relevance vector machines
Authors: Katrakazas, Christos
Quddus, Mohammed A.
Chen, Wen-Hua
Keywords: Algorithms
Learning (Artificial intelligence)
Traffic assignment
Issue Date: 2016
Publisher: Transportation Research Board
Citation: KATRAKAZAS, C., QUDDUS, M.A. and CHEN, W-H., 2016. Real-time classification of aggregated traffic conditions using relevance vector machines. Presented at the Transportation Research Board 95th Annual Meeting, January 10–14, 2016, Washington D.C., USA
Series/Report no.: TRB 95th Annual Meeting Compendium of Papers;16-3417
Abstract: This paper examines the theory and application of a recently developed machine learning technique namely Relevance Vector Machines (RVMs) in the task of traffic conditions classification. Traffic conditions are labelled as dangerous (i.e. probably leading to a collision) and safe (i.e. a normal driving) based on 15-minute measurements of average speed and volume. Two different RVM algorithms are trained with two real-world datasets and validated with one real-world dataset describing traffic conditions of a motorway and two A-class roads in the UK. The performance of these classifiers is compared to the popular and successfully applied technique of Support vector machines (SVMs). The main findings indicate that RVMs could successfully be employed in real-time classification of traffic conditions. They rely on a fewer number of decision vectors, their training time could be reduced to the level of seconds and their classification rates are similar to those of SVMs. However, RVM algorithms with a larger training dataset consisting of highly disaggregated traffic data, as well as the incorporation of other traffic or network variables so as to better describe traffic dynamics, may lead to higher classification accuracy than the one presented in this paper.
Description: This paper was peer-reviewed by TRB and presented at the TRB 95th Annual Meeting, Washington, D.C., January 2016.
Sponsor: This paper was sponsored by TRB committee ABJ70 Standing Committee on Artificial Intelligence and Advanced Computing Applications.
Version: Accepted for publication
URI: https://dspace.lboro.ac.uk/2134/24155
Publisher Link: http://pubsindex.trb.org/view/2016/C/1393271.
Appears in Collections:Conference Papers and Presentations (Architecture, Building and Civil Engineering)

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