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Please use this identifier to cite or link to this item: http://hdl.handle.net/10481/24426

Title: Analysis of traffic accidents on rural highways using Latent Class Clustering and Bayesian Networks
Authors: Oña, Juan de
López Maldonado, Griselda
Mujalli, Randa Oqab
Calvo, Francisco J.
Issue Date: 2013
Abstract: One of the principal objectives of traffic accident analyses is to identify key factors that affect the severity of an accident. However, with the presence of heterogeneity in the raw data used, the analysis of traffic accidents becomes difficult. In this paper, Latent Class Cluster (LCC) is used as a preliminary tool for segmentation of 3229 accidents on rural highways in Granada (Spain) between 2005 and 2008. Next, Bayesian Networks (BNs) are used to identify the main factors involved in accident severity for both, the entire database (EDB) and the clusters previously obtained by LCC. The results of these cluster-based analyses are compared with the results of a full-data analysis. The results show that the combined use of both techniques is very interesting as it reveals further information that would not have been obtained without prior segmentation of the data. BN inference is used to obtain the variables that best identify accidents with killed or seriously injured. Accident type and sight distance have been identify in all the cases analysed; other variables such as time, occupant involved or age are identified in EDB and only in one cluster; whereas variables vehicles involved, number of injuries, atmospheric factors, pavement markings and pavement width are identified only in one cluster.
Sponsorship: TRYSE Research Group, Department of Civil Engineering, University of Granada, Spain
Publisher: Elsevier
Keywords: Cluster analysis
Latent class clustering (LCC)
Bayesian networks (BNs)
Traffic accidents
Injury severity
Road safety
URI: http://hdl.handle.net/10481/24426
ISSN: 0001-4575
Citation: de Oña, J.; López, G.; Mujalli, R.O.; Calvo, F.J. Analysis of traffic accidents on rural highways using Latent Class Clustering and Bayesian Networks. Accident Analysis and Prevention 51: 1–10 (2013). [http://hdl.handle.net/10481/24426]
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