Micro-Level Hotspot Identification at Intersections Using Traffic Conflict Analysis

Abstract

Traditional approaches to identifying traffic crash hotspots have mainly focused on determining dangerous intersections within road networks, overlooking variations in crash risk within intersections. The micro-level crash hotspot analysis addresses this by pinpointing precise high-risk areas, which enables specific. This study aims to identify micro-level hotspots within three signalized intersections using traffic conflict measures derived from drone video. An algorithm calculates conflicts based on various vehicle sizes and conflict angles. The traffic conflict measures applied in this study include time-to-collision (TTC), modified TTC (MTTC), and post-encroachment time (PET). To select the most appropriate conflict measures and determine optimal thresholds at each intersection, we develop crash frequency models using generalized linear modeling (GLM). These selected conflict measures and thresholds are subsequently used to detect micro-level hotspot sections through kernel density. The results demonstrate that the TTC and PET are strongly related to micro-level crash frequencies, with different patterns emerging depending on crash angle and intersection location. Specifically, TTC-based conflicts are highly correlated with rear-end crashes occurring before the stop line, while PET-based conflicts are closely associated with crashes within the intersection, particularly with left-turning movements. This study contributes to intersection safety by identifying traffic conflict measures for micro-level hotspots and offering detailed safety interventions.

Keywords: Crash Risk Hotspot, Traffic Safety, Traffic Conflict Measures, Signalized intersection