A deep embedded clustering method for location-specific driving safety profiling using trajectory data.

ABSTRACT: Understanding driver behavior is critical for enhancing road safety and enabling proactive interventions. While prior studies have largely focused on driver-specific profiles (e.g., aggressive, cautious, safe), such approaches overlook the fact that crashes are often concentrated at specific crash hostspot and corridors. Hence, this study adopts a location-based perspective, analyzing how drivers behave at particular roadway segments to identify their driving patterns. The objectives are: (i) to develop a robust machine learning framework that classifies location-specific driving behavior according to its risk level, and (ii) to create a unified model capable of analyzing multiple vehicle classes together, eliminating the need for separate models for each type. Trajectory data from differe

SUBMITTER: Kushwaha AK 

PROVIDER: S-EPMC12644850 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature