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Mathematical Problems in Engineering
Volume 2017, Article ID 2948750, 6 pages
Research Article

Safety Performance Evaluation of a Three-Leg Unsignalized Intersection Using Traffic Conflict Analysis

Guoqiang Zhang,1,2,3 Jun Chen,1,2,3 and Jingya Zhao1,2,3

1Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing 210096, China
2Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 210096, China
3School of Transportation, Southeast University, Nanjing 210096, China

Correspondence should be addressed to Guoqiang Zhang; moc.361@gnahz.gnaiqoug

Received 15 September 2016; Revised 18 December 2016; Accepted 16 January 2017; Published 23 April 2017

Academic Editor: Benoit Iung

Copyright © 2017 Guoqiang Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Traffic conflicts were used to evaluate safety performance of a three-leg unsignalized intersection. With the aid of a video camera, data were collected at the intersection and 15-second time span was used in each observation to overcome the drawbacks of traditional methods of traffic conflict analysis. Time to collision (TTC), a widely accepted indicator, was used to identify whether an interaction between two vehicles was a traffic conflict. By using Poisson regression, a prediction model for traffic conflicts at the intersection was developed. Based upon the model, assuming that other factors remain constant, when time headway or speed of eastbound traffic on major road, which is crossed by left-turning traffic from minor road, increases, the number of traffic conflicts at the intersection decreases. When volume of left-turning traffic on minor road or speed of left-turning vehicles on minor road increases, the number of traffic conflicts at the intersection increases if other factors remain constant. Explanations for the influence of the factors, which were represented by independent variables of the prediction model, were then analyzed in detail.