Identification of Driving Heterogeneity using Action-chains
Xue Yao Simeon C. Calvert Serge P. Hoogendoorn
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.