Patterns of near-crash events in a naturalistic driving dataset: applying rules mining

TL;DR

This paper investigates the relationship between near-crash events, road geometry, and trip features using association rule mining on naturalistic driving data to uncover underlying patterns.

Contribution

It introduces the application of association rule mining to analyze near-crash events in naturalistic driving datasets, revealing new insights into crash risk factors.

Findings

Identified specific road features associated with near-crash events

Revealed trip patterns linked to increased crash risk

Provided data-driven insights for road safety improvements

Abstract

This study aims to explore the associations between near-crash events and road geometry and trip features by investigating a naturalistic driving dataset and a corresponding roadway inventory dataset using an association rule mining method.