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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/19804

Title: Exploring crash-risk factors using Bayes’ theorem and an optimization routine
Authors: Imprialou, Maria-Ioanna
Maher, Mike
Quddus, Mohammed A.
Keywords: Bayes' theorem
Maximum likelihood estimation
Speed
Volume
Crashes
Issue Date: 2016
Publisher: Transportation Research Board
Citation: IMPRIALOU, M.I., MAHER, M. and QUDDUS, M.A., 2016. Exploring crash-risk factors using Bayes’ theorem and an optimization routine. To be presented at: Transportation Research Board 95th Annual Meeting,10th-14th January 2016, Washington DC.
Abstract: Regression models used to analyse crash counts are associated with some kinds of data aggregation (either spatial, or temporal or both) that may result in inconsistent or incorrect outcomes. This paper introduces a new non-regression approach for analysing risk factors affecting crash counts without aggregating crashes. The method is an application of the Bayes’ Theorem that enables to compare the distribution of the prevailing traffic conditions on a road network (i.e. a priori) with the distribution of traffic conditions just before crashes (i.e. a posteriori). By making use of Bayes’ Theorem, the probability densities of continuous explanatory variables are estimated using kernel density estimation and a posterior log likelihood is maximised by an optimisation routine (Maximum Likelihood Estimation). The method then estimates the parameters that define the crash risk that is associated with each of the examined crash contributory factors. Both simulated and real-world data were employed to demonstrate and validate the developed theory in which, for example, two explanatory traffic variables speed and volume were employed. Posterior kernel densities of speed and volume at the location and time of crashes have found to be different that prior kernel densities of the same variables. The findings are logical as higher traffic volumes increase the risk of all crashes independently of collision type, severity and time of occurrence. Higher speeds were found to decrease the risk of multiple-vehicle crashes at peak-times and not to affect significantly multiple vehicle crash occurrences during off-peak times. However, the risk of single vehicle crashes always increases while speed increases.
Description: This conference paper is closed access.
Version: Accepted for publication
URI: https://dspace.lboro.ac.uk/2134/19804
Publisher Link: http://www.trb.org/AnnualMeeting/AnnualMeeting.aspx
Appears in Collections:Closed Access (Civil and Building Engineering)

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