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More specifically, our study investigated extensively big data with readily available real-time mobile collected from Interstate-75 (I-75) freeway; developed individual division-level models expérience crash likelihood prediction and crash severity prediction. These individual cellule-level models are spatially ensembled to improve performance. To save training costs, each groupe-level model is a lightweight multilayer perceptron. The training of all these lightweight models conserves hundreds of hours compared to the large training of one large model nous all data across various segments. This accomplishment leads to significant reductions in carbon footprints, contributing to environmental conservation. Next, we distilled knowledge from the ensembled models to improve model generalization connaissance higher accuracy and lower false alarms.

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