Data pertaining to Chapter 6 "Human Driving Patterns - A Knowledge-Enhanced Deep Learning Approach for Behaviour Modelling"

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.