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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

            Dez 15, 2023

            Sprecher:innen

            ER

            Edgar Ramirez-Sanchez

            Řečník · 0 sledujících

            SR

            Shreyaa Raghavan

            Řečník · 0 sledujících

            CW

            Cathy Wu

            Řečník · 0 sledujících

            Über

            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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