Literature DB >> 26311202

A random parameters probit model of urban and rural intersection crashes.

Richard Tay1.   

Abstract

Intersections are hazardous locations and many studies have been conducted to identify the factors contributing to the frequency and severity of intersection crashes. However, little attention has been devoted to investigating the differences between crashes at urban and rural intersections, which have different road, traffic and environmental characteristics. By applying a random parameters probit model to the data from the Canadian Province of Alberta between 2008 and 2012, we find that urban intersection crashes are more likely to be associated with hit and run behaviours, roads with higher traffic volume, wet surfaces, four lanes and skewed intersections, and crashes on weekdays and off-peak hours, whereas rural crashes are likely to be associated with increases in fatalities and injuries, roads with higher speed limits, special road features, exit and entrance terminals, gravel, curvature and two lanes, crashes during weekends, peak hours and night-time, run-off-road crashes, and police visit to crash scene. Hence, road safety professionals in urban and rural areas should consider these differences when designing and implementing counter-measures to improve intersection safety, especially their safety audits and reviews, enforcement activities and education campaigns, to target the more vulnerable times and locations in the different areas.
Copyright © 2015 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Intersection crashes; Random parameters probit model; Rural intersection; Urban intersection

Mesh:

Year:  2015        PMID: 26311202     DOI: 10.1016/j.aap.2015.07.013

Source DB:  PubMed          Journal:  Accid Anal Prev        ISSN: 0001-4575


  1 in total

1.  Decision Tree Ensemble Method for Analyzing Traffic Accidents of Novice Drivers in Urban Areas.

Authors:  Serafín Moral-García; Javier G Castellano; Carlos J Mantas; Alfonso Montella; Joaquín Abellán
Journal:  Entropy (Basel)       Date:  2019-04-03       Impact factor: 2.524

  1 in total

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