Web: http://arxiv.org/abs/2205.05197

May 12, 2022, 1:11 a.m. | Artur Grigorev, Adriana-Simona Mihaita, Seunghyeon Lee, Fang Chen

cs.LG updates on arXiv.org arxiv.org

Predicting the duration of traffic incidents is a challenging task due to the
stochastic nature of events. The ability to accurately predict how long
accidents will last can provide significant benefits to both end-users in their
route choice and traffic operation managers in handling of non-recurrent
traffic congestion. This paper presents a novel bi-level machine learning
framework enhanced with outlier removal and intra-extra joint optimisation for
predicting the incident duration on three heterogeneous data sets collected for
both arterial roads …

arxiv bi framework learning machine machine learning prediction

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