Effect of vehicle size on crash risk in a heterogeneous traffic scenario: a bivariate extreme value approach

Abstract

Most traffic conflict indicators are defined for car-following scenarios where a follower vehicle interacts with a leader vehicle in one-dimensional space. However, vehicles do interact in a two-dimensional space especially in a heterogeneous traffic environment. Further, designating an interaction as risky depends on the interacting leader-follower (LF) pairs.

Conflict indicators namely Time-to-Collision (TTC) and lateral gap which quantifies longitudinal and lateral interactions respectively, were computed from video recordings at four accident black spots on four-lane divided highways. Conflict in two-dimensional space was modelled for various LF-pairs using the Bivariate Extreme Value function of these two conflict indicators. Crash risk was estimated for each LF-pairs separately. Results show that cars and light commercial vehicles exhibit higher crash risk as compared to two-wheelers and motorized three-wheelers. The proposed framework can be used for more accurate risk assessment and calibration of collision warning systems in lane free mixed traffic conditions.