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

TL;DR

This paper presents a kernel-based traffic reconstruction method to accurately identify stop-and-go congestion events using sparse data, aiding sustainable traffic management and policy decisions.

Contribution

It introduces a novel kernel method combined with bootstrapping for precise SAG detection from limited traffic data, addressing a key data scarcity challenge.

Findings

Successfully identified SAGs in California highway data

Demonstrated the method's ability to quantify reconstruction uncertainty

Provides a foundation for data-driven traffic sustainability policies

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 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.