Data pertaining to Chapter 2 "Driving Heterogeneity Identification using Machine Learning: A Review and Framework for Analysis"

DOI:10.4121/528fa9ed-0ccc-4e19-b1c6-5bdfc4e9a7c6.v1
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/528fa9ed-0ccc-4e19-b1c6-5bdfc4e9a7c6

Datacite citation style

Yao, Xue (2025): Data pertaining to Chapter 2 "Driving Heterogeneity Identification using Machine Learning: A Review and Framework for Analysis". Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/528fa9ed-0ccc-4e19-b1c6-5bdfc4e9a7c6.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

This dataset supports the paper “Driving Heterogeneity Identification using Machine Learning: A Review and Framework for Analysis” (Chapter 2 of the PhD dissertation). The research provides a systematic review of existing machine learning (ML)-based approaches for identifying driving heterogeneity. The review organises key concepts and categorisations of driving heterogeneity, highlights strengths and drawbacks of various methods, and outlines applications of identification analysis. Based on the literature review, a structured framework that guides the ML-based identification process is proposed, including data collection and pre-processing, feature selection, ML model training, and performance evaluation. The dataset includes summary statistics on data collection methods over time and an overview of traffic variables used in the reviewed literature. It is provided as a zipped folder containing files in .ipynb and .xlsx formats, along with a ch2_Readme.txt file that explains the dataset structure and provides usage instructions.

History

  • 2025-07-07 first online, published, posted

Publisher

4TU.ResearchData

Format

.xlsx, *.pdf, *.txt, *.csv i.e., script/.py, spreadsheet/.xlsx, image/.jpeg, image/.pdf

Organizations

TU Delft, Faculty of Civil Engineering and Geosciences, Department of Transport and Planning, Traffic Systems Engineering