eprintid: 1474935
rev_number: 27
eprint_status: archive
userid: 608
dir: disk0/01/47/49/35
datestamp: 2016-02-23 10:16:52
lastmod: 2025-02-04 07:10:09
status_changed: 2016-02-23 10:16:52
type: proceedings_section
metadata_visibility: show
creators_name: Maher, MJ
creators_name: Imprialou, M-I
creators_name: Quddus, M
title: Exploring crash-risk factors using Bayes’ theorem and an optimization routine
ispublished: pub
divisions: A01
divisions: B04
divisions: C05
divisions: F44
keywords: Bayes' theorem; Crash characteristics; Crash risk forecasting; Crash severity; Mathematical models; Risk assessment; Speed; Traffic volume
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
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.
date: 2016-01
date_type: published
publisher: Transportation Research Board
official_url: https://trid.trb.org/View/1393307
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1112507
lyricists_name: Maher, Michael
lyricists_id: MJMAH36
actors_name: Maher, Michael
actors_id: MJMAH36
actors_role: owner
full_text_status: public
place_of_pub: Washington DC, USA
event_title: 95th Annual Meeting of the Transportation Research Board
event_location: Washington DC
event_dates: 10 January 2016 - 14 January 2016
institution: 95th Annual Meeting of the Transportation Research Board
citation:        Maher, MJ;    Imprialou, M-I;    Quddus, M;      (2016)    Exploring crash-risk factors using Bayes’ theorem and an optimization routine.                     In:   (Proceedings) 95th Annual Meeting of the Transportation Research Board.   Transportation Research Board: Washington DC, USA.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/1474935/3/TRB%202016%20Imprialou%20Maher%20Quddus%20v8.pdf