Driving heterogeneity identification using machine learning

Abstract

Driving heterogeneity significantly influences traffic performance, contributing to traffic disturbances, increased crash risks, and inefficient fuel use and emissions. With the growing availability of driving behaviour data, Machine Learning (ML) techniques have become widely used for analysing driving behaviour and identifying heterogeneity. This paper presents a systematic review of current ML-based methods for driving heterogeneity identification. The review organises key concepts and categorisations of driving heterogeneity, highlights strengths and drawbacks of various methods, and outlines applications of identification analysis. Based on the literature review, we propose a structured framework that guides the ML-based identification process. The framework starts with an extensive data collection and rigorous pre-processing process, followed by feature selection techniques that identify features most indicative of driving behaviours. Sophisticated models including supervised, unsupervised, semi-supervised, and reinforcement learning techniques are discussed with multi-perspective performance evaluation. This paper provides a comprehensive reference for researchers and practitioners to understand driving heterogeneity, supporting the development of data-driven solutions for improving traffic management and road safety.