This is the implementation of a paper submitted to IEEE Transactions on Intelligent Transportation Systems [Paper]
Due to the continuing colossal socio-economic losses caused by traffic accidents, it is of prime importance to precisely forecast the traffic accident risk for reduce future accidents. In this paper, we use dangerous driving statistics from driving log data and multi-graph learning to enhance predictive performance. We first conduct geographical and temporal correlation analyses to quantify the relationship between dangerous driving and actual accidents. Then, to learn similarities between districts in addition to the traditional adjacency matrix, we explicitly model the spatio-temporal contextual relationships with heterogeneous environmental data, including the dangerous driving behavior. A graph model is generated for each type of the relationships. Ultimately, we propose an end-to-end framework, called MG-TAR, to effectively learn the association of multiple graphs for accident risk prediction by adopting multi-view graph neural networks with an interview attention module. Thorough experiments on ten real-world datasets show that, compared with state-of-the-art methods, MG-TAR reduces the error of predicting the accident risk by up to 23% and improves the accuracy of predicting the most dangerous areas by up to 29%.
- Digital Tachograph (Driving Log) Data: cannot be publicly accessible
- For demonstration purpose, we partially provide the aggregated number of classified dangerous driving cases in the
datasetsfolder. - If you are interested in the original data, there is a sample file provided here by Korea Transportation Safety Authority.
- For demonstration purpose, we partially provide the aggregated number of classified dangerous driving cases in the
For package installation: pip install -r requirements.txt
For model testing: Example Run.ipynb
TBD