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  • title: Data-Driven Traffic Reconstruction and Kernel Methods for Identifying Stop-and-Go Congestion
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            Data-Driven Traffic Reconstruction and Kernel Methods for Identifying Stop-and-Go Congestion
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            Data-Driven Traffic Reconstruction and Kernel Methods for Identifying Stop-and-Go Congestion

            Dec 15, 2023

            Speakers

            ER

            Edgar Ramirez-Sanchez

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            SR

            Shreyaa Raghavan

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            CW

            Cathy Wu

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            About

            Identifying stop-and-go events (SAGs) in traffic flow presents an important avenue for advancing data-driven research for climate change mitigation and sustainability, owing to their substantial impact on carbon emissions, travel time, fuel consumption, and roadway safety. In fact, SAGs are estimated to account for 33-50% of highway driving externalities. However, insufficient attention has been paid to precisely quantifying where, when, and how much these SAGs take place– necessary for downstre…

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            NeurIPS 2023

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