Designs for Safer Signal-Controlled Intersections by Statistical Analysis of Accident Data at Accident Blacksites

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)111302-111314
Number of pages13
Journal / PublicationIEEE Access
Volume7
Online published11 Jul 2019
Publication statusPublished - 2019

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Abstract

This paper describes the collection and statistical analysis of accident counts and intersection layout geometries at a range of signal-controlled intersections, with the aim of improving safety at these sites. Negative binomial regression analysis is conducted to relate the accident count data as a dependent variable, with various independent variables to capture the intersection layout and lane-marking patterns. Statistically significant variables are identified, and their individual effects on accident counts are analyzed. Although the accident-prediction models for signalized intersections have been extensively investigated, this paper also considers the effects of shared lane markings, which is a new approach. The results of this paper show that the shared lane markings are indeed a statistically significant predictor of the number of accidents. It was found that the accident counts at signal-controlled intersections could be reduced by altering the lane-marking patterns using a combination of well-established lane-based design methods and new governing constraint sets to enhance the safety controls for turning traffic derived from our statistical analysis. These new lane-marking patterns also satisfy engineering performance requirements. The intersections in Hong Kong were investigated as illustrative case studies, and the numerical results show a substantial decrease in the predicted accident counts, with an acceptable tradeoff in the reduction of overall intersection capacity.

Research Area(s)

  • Accident prevention, road accidents, statistical analysis, traffic signal control

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