Identifying Stop-and-Go Congestion with Data-Driven Traffic Reconstruction
Other Workshops: NeurIPS Tackling Climate Change with Machine Learning Workshop
Keywords: Transportation, Congestion, Stop-and-Go Waves, Highway Emissions, Sustainable Roads, Traffic Reconstruction, Kernels, Uncertainty Quantification, Robustness
TL;DR: We propose the use of traffic reconstruction as a way to generate rich vehicle trajectory information and enable the deployment of complex ML techniques for detecting and mitigating stop-and-go waves.
Abstract: 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 often 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 spatiotemporal 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 the sustainability of traffic systems.
Submission Number: 25
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