Developing rear-end and side-swipe conflict prediction models for urban signalized intersections under disordered traffic conditions

Abstract

A Traffic Conflict Technique is a proactive approach that identifies observable critical vehicle interactions (conflict) that could have led to a crash. This paper develops conflict-based safety performance functions (SPFs) to predict the number of traffic conflicts at the signal cycle level in mixed traffic conditions with poor lane discipline (disordered traffic conditions). 9586 vehicle trajectories were extracted from traffic video data collected from 4 signalized intersections in India. Critically interacting vehicle pairs were estimated using Time to Collision (TTC). The conventional surrogate measure is modified into a 2-dimensional surrogate approach which captures the conflicts by incorporating the vehicle dimension, heading direction, position, speed, and acceleration in the longitudinal and lateral direction between vehicles. Rear-end and side-swipe conflicts are identified at varying threshold values to incorporate severity. Rear-end conflict SPFs show that higher conflict occurrence is expected during signal cycles with more traffic volume, higher vehicle arrival speed, more right-turning traffic, and a lower platoon ratio. At severe threshold (TTC ≤ 1 s), one 1 m/s increase in speed of vehicle increases the expected number of rear-end conflicts by 6%. Furthermore, SPFs show that severe side-swipe conflicts are expected during signal cycles with more lane-changing maneuvers and right-turning vehicles. For TTC ≤ 1 s, one unit increase in the number of lane changes would increase the expected number of sides-wipe conflicts by 7.9%. The finding of this study can be most beneficially used for arriving at policy measures and improving the safety of signalized intersections in disordered traffic conditions.