A Comparative Analysis for Unraveling the Dynamics of Single-Vehicle versus Multi-Vehicle Crashes

Abstract: This paper presents a comprehensive study aimed at understanding the dynamics of single-vehicle and multi-vehicle crashes through a binary classification approach. By harnessing high-resolution, multisource data, including rich traffic data captured by Weight-in-Motion stations, weather conditions, roadway attributes, and pavement properties, we delve into the distinguishing characteristics of these two types of crashes. Particularly, a meticulous data fusion approach is employed to integrate the diverse data sources, enabling a holistic investigation of the factors contributing to each crash type. Through extensive data processing and feature engineering, key differentiating factors between single-vehicle and multi-vehicle crashes are identified. The results of the study provide crucial insights into the underlying mechanisms of the two distinct crash types.  By shedding light on the specific influencing factors, our findings pave the way for the development and implementation of more efficient and tailored safety measures, which can play a vital role in enhancing road safety and reducing accident rates and severities. Our research contributes to the advancement of evidence-based road safety practices, fostering a safer transportation landscape for all road users.