Unraveling the dynamics of single-vehicle versus multi-vehicle crashes: a comparative analysis through binary classification

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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, multi-source data, including high-resolution traffic profile data captured by weigh-in-motion stations, weather conditions, roadway attributes, and pavement properties, we delved into distinctive characteristics of the two crash types. Particularly, a meticulous data fusion approach was applied to integrate the diverse data sources, enabling a holistic investigation of influential factors. Framing it as a classification task, key factors differentiating between single-vehicle and multi-vehicle crashes were identified. The results of the study provide valuable insights into the underlying mechanisms of the two distinct crash types, supporting the development of targeted safety measures.

Keywords

single-vehicle crashes / multi-vehicle crashes / traffic safety / binary classification / regularized logistic regression / multi-source data / data fusion

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