PhD dissertation: Driving Heterogeneity in Traffic Flow Theory: An Action-based Framework for Identification, Modelling, and Simulation
DOI:10.4121/75ca8ce7-dcab-49cc-93f8-e1a6a15bd244.v1
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DOI: 10.4121/75ca8ce7-dcab-49cc-93f8-e1a6a15bd244
DOI: 10.4121/75ca8ce7-dcab-49cc-93f8-e1a6a15bd244
Datacite citation style
Yao, Xue (2025): PhD dissertation: Driving Heterogeneity in Traffic Flow Theory: An Action-based Framework for Identification, Modelling, and Simulation. Version 1. 4TU.ResearchData. collection. https://doi.org/10.4121/75ca8ce7-dcab-49cc-93f8-e1a6a15bd244.v1
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Collection
This is a collection of all datasets included as individual chapters in the PhD dissertation.
History
- 2025-07-07 first online, published, posted
Publisher
4TU.ResearchDataReferences
Funding
- Delft University of Technology
Organizations
TU Delft, Faculty of Civil Engineering and Geosciences, Department of Transport and Planning, Traffic Systems EngineeringDATASETS
- [dataset] Data pertaining to Chapter 6 "Human Driving Patterns - A Knowledge-Enhanced Deep Learning Approach for Behaviour Modelling"
- [dataset] Data pertaining to Chapter 2 "Driving Heterogeneity Identification using Machine Learning: A Review and Framework for Analysis"
- [dataset] Data pertaining to Chapter 3 "Investigation on Car-Following Heterogeneity and Its Impacts on Traffic Flow Performance"
- [dataset] Data pertaining to Chapter 4 "Identification of Driving Heterogeneity using Action-chains"
- [dataset] Data pertaining to Chapter 5 "A Novel Framework for Understanding and Identifying Driving Heterogeneity through Action Patterns"
- [dataset] Data pertaining to Chapter 7 "A Pattern-based Framework for Modelling Driving Heterogeneity and Traffic Flow Simulation"