Data pertaining to Chapter 4 "Identification of Driving Heterogeneity using Action-chains"

This dataset accompanies the paper “Identification of Driving Heterogeneity using Action-chains” (Chapter 4 of the PhD dissertation). The research introduces a comprehensive framework for identifying driving heterogeneity from an action perspective. Driving trajectories are identified into Action phases with physical meanings based on rule-based segmentation techniques. The Action chain concept is then introduced by implementing the Action phase transition probability. Evaluating using a naturalistic dataset indicates that this approach effectively identifies driving heterogeneity while providing clear interpretations. The research includes data preprocessing to clean data, rule-based segmentation to extract Action phases, driving behaviour map establishment, Action chain modelling, and heterogeneous traffic flow evaluation. The dataset is provided as a zipped folder containing four Jupyter notebooks (.ipynb) and supporting files in .xlsx, .csv, .mat, .m, .txt, and .pdf formats. A ch4_Readme.txt file is included to guide users on the structure, usage, and purpose of the data.