Data-Driven Traffic Reconstruction and Kernel Methods for Identifying Stop-and-Go Congestion
This addresses the need for precise quantification of stop-and-go events to support data-driven decision-making for climate change mitigation and sustainability in traffic systems, though it appears incremental as it builds on existing traffic reconstruction techniques.
The study tackled the problem of identifying stop-and-go congestion events in traffic flow, which account for 33-50% of highway driving externalities, by developing a kernel-based method with bootstrapping for uncertainty quantification, showing promise on California highway data.
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 downstream decision making, such as intervention design and policy analysis. A key challenge is that the data available to researchers and governments are typically sparse and aggregated to a granularity that obscures SAGs. To overcome such data limitations, this study thus explores the use of traffic reconstruction techniques for SAG identification. In particular, we introduce a kernel-based method for identifying spatio-temporal features in traffic and leverage bootstrapping to quantify the uncertainty of the reconstruction process. Experimental results on California highway data demonstrate the promise of the method for capturing SAGs. This work contributes to a foundation for data-driven decision making to advance sustainability of traffic systems.