TY - DATA T1 - Data pertaining to Chapter 5 "A Novel Framework for Understanding and Identifying Driving Heterogeneity through Action Patterns" PY - 2025/07/07 AU - Xue Yao UR - DO - 10.4121/f0d9d36b-6170-4a0d-836f-6e3bd8560ae9.v1 KW - Driving behaviour analysis KW - Driving heterogeneity KW - Driving pattern classification KW - Attention-based LSTMs N2 -

This dataset supports the paper “A Novel Framework for Understanding and Identifying Driving Heterogeneity through Action Patterns” (Chapter 5 of the PhD dissertation). The study focuses on data analysis and behavioural modelling, introducing a new framework for identifying driving heterogeneity based on underlying action patterns in driver behaviour. The framework includes three processes: Action phase extraction, Action pattern calibration, and Action pattern classification. Evaluation of the framework on a large-scale naturalistic driving dataset reveals six distinct Action patterns. The implementation of the attention mechanism to LSTM models significantly enhanced both the accuracy and time efficiency of Action pattern identification. The data was generated and processed using rule-based segmentation, unsupervised learning, feature extraction, and supervised learning techniques in Python. It is provided as a zipped folder containing two subfolders, with files in .xlsx, .csv, .mat, .m, .txt, and .pdf formats. A ch5_Readme.txt file is included to explain the structure of the data and provide instructions for use.

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