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.

翻译：当前识别驾驶异质性的方法在捕捉驾驶特征多样性和从驾驶行为机制角度理解基本模式方面面临挑战。本研究提出了一种从动作链视角识别驾驶异质性的综合框架。首先，提出了一种考虑驾驶行为物理意义的基于规则的分割技术。其次，基于分割结果创建了包含多种驾驶行为模式描述的动作阶段库。然后，通过实现动作阶段转移概率引入动作链概念，并提出了评估驾驶异质性的方法。采用真实数据集进行评估，我们的方法能有效识别个体驾驶者和交通流中的驾驶异质性，同时提供清晰的解释。这些见解有助于发展准确的驾驶行为理论和交通流模型，最终改善交通性能，并可能带来道路通行能力和安全性等方面的提升。