Random Forest Models for Identifying Motorway Rear-End Crash Risks Using Disaggregate Traffic Data and Meteorological Information

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We propose a novel system leveraging deep learning-based methods to predict urban traffic accidents and estimate their severity. The major challenge is the data imbalance problem in traffic accident prediction. The problem is caused by numerous zero values ...

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Early and accurate detection of epileptic seizures is an extremely important therapeutic goal due to the severity of complications it can prevent. To this end, a low-power machine learning-based seizure detection implemented on an FPGA is proposed in this ...

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Traffic control for large-scale urban road networks remains a challenging problem. Aggregated dynamical models of city-scale traffic, based on the macroscopic fundamental diagram (MFD), enable development of model-based perimeter control methods. Involving ...

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Random Forest Models for Identifying Motorway Rear-End Crash Risks Using Disaggregate Traffic Data and Meteorological Information

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