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DOI: 10.4121/07a8e039-ca91-4acf-9d84-b6513155dba6

Yao, Xue (2025): Data pertaining to Chapter 6 "Human Driving Patterns - A Knowledge-Enhanced Deep Learning Approach for Behaviour Modelling". Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/07a8e039-ca91-4acf-9d84-b6513155dba6.v1

Categories

• Civil Engineering
• Environmentally Sustainable Transport
• Interdisciplinary Engineering
• Ground Transport
• Engineering
• Transport

Keywords

Action patterns Attention LSTM. Driving behaviour modelling driving pattern prediction Knowledge-enhanced models

Licence

CC BY-NC 4.0

Interoperability

• RO-Crate Metadata

Collection

• PhD dissertation: Driving Heterogeneity in Traffic Flow Theory: An Action-based Framework for Identification, Modelling, and Simulation

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by Xue Yao

This dataset accompanies the paper “Human Driving Patterns – A Knowledge-Enhanced Deep Learning Approach for Behaviour Modelling” (Chapter 6 of the PhD dissertation). The research focuses on data-driven modelling of longitudinal driving behaviour using a novel knowledge-enhanced deep learning framework. It aims to integrate expert knowledge with deep learning to improve the interpretability and accuracy of driver behaviour models. A Knowledge-Enhanced Attention LSTM (KE-ALSTM) model to predict transitions and durations of Action patterns. Graph-based and distribution-based knowledge are integrated to improve DL model performance. Evaluation of real-world data demonstrates that KE-ALSTM outperforms baseline models, demonstrating the value of incorporating domain knowledge

to enhance deep-learning models in driving behaviour analysis. The dataset was created and processed through a combination of data preprocessing, feature extraction, and model training in MATLAB and Python. It is provided as a zipped folder containing files in .xlsx, .csv, .mat, .m, .txt, and .pdf formats. A ch6_Readme.txt file is included to guide users on how to access and use the data for reproduction and further research.

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