Identification of Hazardous Situations using Kernel Density Estimation Method Based on Time to Collision, Case study: Left-turn on Unsignalized Intersection

Abstract

The first step in improving traffic safety is identifying hazardous situations. Based on traffic accidents’ data, identifying hazardous situations in roads and the network is possible. However, in small areas such as intersections, especially in maneuvers resolution, identifying hazardous situations is impossible using accident’s data. In this paper, time-to-collision (TTC) as a traffic conflict indicator and kernel density estimation (KDE) method have been used to identify hazardous situations. KDE applies smooth function on critical TTC value events, this surface indicates risk changes. The maximum quantity of this function represents the hazardous situations. To assess and implement the presented method, left-turn on unsignalized intersection has been chosen. TTC data are determined by automated video analysis and coordinating TTC smaller than threshold value was used as input data in KDE method. Hazardous situations have been identified and the factors that caused them have been recognized using these results and performing safety audit. Two countermeasures are proposed to improve safety of left-turn in study location.