Data pertaining to Chapter 7 "A Pattern-based Framework for Modelling Driving Heterogeneity and Traffic Flow Simulation"

This dataset supports the research presented in the paper <em>“A Pattern-based Framework for Modelling Driving Heterogeneity and Traffic Flow Simulation”</em> (Chapter 7 of the corresponding PhD dissertation). The study aims to analyse the impact of driving heterogeneity on traffic flow using a novel pattern-based modelling and simulation framework. The research combines data analysis, behavioural modelling, and simulation-based experiments to examine how different driving patterns affect traffic performance. A bi-level modelling approach is developed to model high-level behavioural pattern transitions and low-level vehicle dynamics, enabling the capture of both inter- and intra-driver variability in longitudinal driving behaviour in the micro-simulation. Evaluation on real-world data demonstrates its effectiveness in revealing how different levels of driving heterogeneity affect traffic safety, energy efficiency, and traffic stability. Data was generated and processed through modelling and simulations, with analysis conducted using Python. The dataset includes a zipped folder containing structured files in <code>.xlsx</code>, <code>.csv</code>, <code>.mat</code>, <code>.m</code>, <code>.txt</code>, and <code>.pdf</code> formats. Two Jupyter notebooks (<code>.ipynb</code>) and a <code>ch7_Readme.txt</code> file provide guidance on reproducing the analyses.