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A Traffic Flow Simulation Framework for Learning Driver Heterogeneity from Naturalistic Driving Data using Autoencoders

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This paper proposes a novel data-centric framework for microscopic traffic flow simulation with intra and inter driver heterogeneity. We utilized a naturalistic driving corpus of 46 different drivers to learn and model the behavior divergence of Japanese drivers. First. ego-driver behavior signals are used to extract unique features of each driver with an auto-encoder. https://toyscyclers.shop/product-category/games-toys/
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