Data-Driven Traffic Reconstruction and Kernel Methods for Identifying
  Stop-and-Go Congestion
Edgar Ramirez Sanchez Shreyaa Raghavan Cathy Wu
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.