Identification of Hazardous Situations using Kernel DensityEstimation Method Based on Time to Collision, Case study: Leftturnon Unsignalized Intersection

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, insmall areas such as intersections, especially in maneuvers resolution, identifying hazardous situationsis impossible using accident’s data. In this paper, time-to-collision (TTC) as a traffic conflictindicator and kernel density estimation (KDE) method have been used to identify hazardoussituations. KDE applies smooth function on critical TTC value events, this surface indicates riskchanges. The maximum quantity of this function represents the hazardous situations. To assess andimplement the presented method, left-turn on unsignalized intersection has been chosen. TTC dataare determined by automated video analysis and coordinating TTC smaller than threshold valuewas used as input data in KDE method. Hazardous situations have been identified and the factorsthat caused them have been recognized using these results and performing safety audit. Two countermeasuresare proposed to improve safety of left-turn in study location

Identification of Hazardous Situations using Kernel DensityEstimation Method Based on Time to Collision, Case study: Leftturnon Unsignalized Intersection Keywords:

KDE method , time-to-collision , hazardous situations , traffic conflict