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.

KEYWORDS:

• Surrogate safety
• Non-lane-based traffic
• extreme value theory
• heterogeneous traffic
• traffic conflicts

Acknowledgements

The authors appreciate the assistance provided by the Department of Civil Engineering, IIT (BHU). We would like to thank two anonymous reviewers at Transportation Letters for their insightful comments on earlier versions of this paper.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.