*** Code and data underlying the publication: Data-driven Semi-supervised Machine Learning with Safety Indicators for Abnormal Driving Behavior Detection ***
Authors: Dong, Y., Zhang, L., Farah, H., Zgonnikov, A., & van Arem, B.

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


***General Introduction***
This is the code and processed data related to the publication:
Dong, Y., Zhang, L., Farah, H., Zgonnikov, A., & van Arem, B. (2023). Data-driven Semi-supervised Machine Learning with Surrogate Safety Measures for Abnormal Driving Behavior Detection. arXiv preprint arXiv:2312.04610. https://arxiv.org/abs/2312.04610



The original data is from https://github.com/UCF-SST-Lab/UCF-SST-CitySim1-Dataset
The csv files are the processed data.

The codes make use of open-sourced implementation of HELM and other semi-supervised learning algorithms.
After setting up the folder and fetching the data, one can simply run the code with the specific function (identified by their names) get the relevant results.

Details about the implementation are demonstrated in the paper and the annotations.