Data pertaining to Chapter 3 "Investigation on Car-Following Heterogeneity and Its Impacts on Traffic Flow Performance"

This dataset supports the paper <em>“Investigation on Car-Following Heterogeneity and Its Impacts on Traffic Flow Performance”</em> (Chapter 3 of the PhD dissertation). The study focuses on behavioural modelling and simulation, aiming to investigate car-following heterogeneity and assess its effects on traffic safety and sustainability. The framework incorporates rigorous driving style classification using a semi-supervised learning technique and a micro-simulation process that includes 66 fine-grained traffic scenarios exhibiting varying degrees of heterogeneity. Based on two distinct real-world datasets, the impacts of driving heterogeneity are effectively elucidated from the mechanism of underlying characteristics of driving behaviour and traffic flow dynamics. The data is organised into three folders, corresponding to model parameter calibration, behavioural classification, and traffic flow simulation components of the research. The data was generated and processed using MATLAB and includes files in <code>.xlsx</code>, <code>.csv</code>, <code>.mat</code>, <code>.m</code>, <code>.txt</code>, and <code>.pdf</code> formats. A <code>ch3_Readme.txt</code> file is provided to guide users through the dataset structure and usage.