Evaluation of the feasibility of a machine learning-based method for estimating lane change behavior from private probe data

Abstract

In recent years, despite various safety measures, traffic accidents remain a frequent and critical issue, highlighting the urgent need for effective countermeasures. On urban expressways, merging and diverging sections are particularly prone to complex vehicle interactions, making it crucial to identify these accident-prone areas. One promising approach is the use of vehicle probe data, which provides individual trajectories. While previous studies have proposed methods for estimating lane changes from kinematic features such as speed and acceleration, the accuracy of these methods is often compromised by variations in road geometry and traffic conditions. This study proposes a machine learning-based method for estimating lane change behavior from private probe data, focusing on straight sections of urban expressways. The proposed method achieved an accuracy exceeding 90% in detecting actual lane changes. This result demonstrates the method’s high potential for micro-level traffic safety analysis and management.