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Driver Fatigue Prediction using Randomly Activated Neural Networks for Smart Ridesharing Platforms
April 17, 2024, 4:42 a.m. | Sree Pooja Akula, Mukund Telukunta, Venkata Sriram Siddhardh Nadendla
cs.LG updates on arXiv.org arxiv.org
Abstract: Drivers in ridesharing platforms exhibit cognitive atrophy and fatigue as they accept ride offers along the day, which can have a significant impact on the overall efficiency of the ridesharing platform. In contrast to the current literature which focuses primarily on modeling and learning driver's preferences across different ride offers, this paper proposes a novel Dynamic Discounted Satisficing (DDS) heuristic to model and predict driver's sequential ride decisions during a given shift. Based on DDS …
abstract arxiv cognitive contrast cs.lg cs.ma current driver drivers efficiency impact literature modeling networks neural networks platform platforms prediction smart type
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