Modelling Animal-Vehicle Collision Counts across Large Networks Using a Bayesian Hierarchical Model with Time-Varying Parameters

TL;DR

This paper introduces a scalable Bayesian hierarchical model with time-varying parameters to analyze animal-vehicle collision counts across large road networks, accounting for seasonality, low counts, and unknown exposure, aiding targeted prevention strategies.

Contribution

The study develops a novel Bayesian hierarchical model incorporating nonparametric exposure modeling and efficient Gibbs sampling, suitable for large, zero-inflated AVC datasets with seasonal patterns.

Findings

Model effectively captures seasonality and zero-inflation in AVC data.

Identifies high-risk locations and key factors influencing collisions.

Demonstrates scalability to large datasets with over 100,000 segments.

Abstract

Animal-vehicle collisions (AVCs) are common around the world and result in considerable loss of animal and human life, as well as significant property damage and regular insurance claims. Understanding their occurrence in relation to various contributing factors and being able to identify locations of high risk are valuable to AVC prevention, yielding economic, social and environmental cost savings. However, many challenges exist in the study of AVC datasets. These include seasonality of animal activity, unknown exposure (i.e., the number of animal crossings), very low AVC counts across most sections of extensive roadway networks, and computational burdens that come with discrete response analysis using large datasets. To overcome these challenges, a Bayesian hierarchical model is proposed where the exposure is modeled with nonparametric Dirichlet process, and the number of segment-level AVCs is assumed to follow a Binomial distribution. A P\'olya-Gamma augmented Gibbs sampler is derived to estimate the proposed model. By using the AVC data of multiple years across about 100,000 segments of state-controlled highways in Texas, U.S., it is demonstrated that the model is scalable to large datasets, with a preponderance of zeros and clear monthly seasonality in counts, while identifying high-risk locations (for application of design treatments, like separated animal crossings with fencing) and key explanatory factors based on segment-specific factors (such as changes in speed limit) can be done within the modelling framework, which provide useful information for policy-making purposes.