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

This dataset accompanies the paper <em>“Identification of Driving Heterogeneity using Action-chains”</em> (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 (<code>.ipynb</code>) and supporting files in <code>.xlsx</code>, <code>.csv</code>, <code>.mat</code>, <code>.m</code>, <code>.txt</code>, and <code>.pdf</code> formats. A <code>ch4_Readme.txt</code> file is included to guide users on the structure, usage, and purpose of the data.