Identification of Driving Heterogeneity using Action-chains
This work addresses the challenge of capturing diverse driving characteristics for traffic performance improvement, though it appears incremental in method.
The study tackled the problem of identifying driving heterogeneity by proposing a comprehensive framework from an Action-chain perspective, which effectively identified heterogeneity for individual drivers and traffic flow using real-world datasets.
Current approaches to identifying driving heterogeneity face challenges in capturing the diversity of driving characteristics and understanding the fundamental patterns from a driving behaviour mechanism standpoint. This study introduces a comprehensive framework for identifying driving heterogeneity from an Action-chain perspective. First, a rule-based segmentation technique that considers the physical meanings of driving behaviour is proposed. Next, an Action phase Library including descriptions of various driving behaviour patterns is created based on the segmentation findings. The Action-chain concept is then introduced by implementing Action phase transition probability, followed by a method for evaluating driving heterogeneity. Employing real-world datasets for evaluation, our approach effectively identifies driving heterogeneity for both individual drivers and traffic flow while providing clear interpretations. These insights can aid the development of accurate driving behaviour theory and traffic flow models, ultimately benefiting traffic performance, and potentially leading to aspects such as improved road capacity and safety.