Modeling car and heavy commercial vehicle crashes on two-lane rural highways using the Poisson-Tweedie regression approach

Abstract

This article develops vehicle type–based crash-prediction models for cars and heavy commercial vehicles (HCVs) as a function of the curve geometry and vehicle-based design consistency criteria under heterogeneous traffic conditions on two-lane, two-way rural highways, specifically in hilly terrains. A National Highway (NH-953) connecting Netrang and Rajpipla in India was selected. There are 38 curves in the study section, each having a different curve geometry. Speed data were collected using the radar gun for cars and HCVs. The geometric design consistency was evaluated using Criterion I (the difference between operating and design speeds). The results show that 53% of the curves for cars have good consistency, compared to 32% and 29% of the curves for HCVs, which have fair and poor consistency, respectively. The Poisson-Tweedie regression technique, which provides a unified framework to model over-dispersed, under-dispersed, zero-inflated, count-data, and multiple-response variables, was used to develop the crash prediction models. The results revealed that crashes (cars and HCVs) decrease as the curve radius, deflection angle, and length increase. Similarly, as the tangent length increases, the difference between operating and design speeds increases, making inconsistent highway alignment, resulting in increased chances of crashes. The results of the present study can help highway authorities to evaluate highway alignment consistency and develop corresponding proactive strategies to improve highway safety.