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ViewAttendees 5

IPOD Abstract for presentation (Poster or Podium)

Data Sensing and Analytics

1667154 - Data-Driven Traffic Reconstruction for Identifying Stop-and-Go Waves

Monday, June 17, 2024
10:15 AM - 10:25 AM ET
Room: 223
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 and their avoidable costs. This work contributes to a foundation for data-driven decision-making to advance the sustainability of traffic systems and hopes to inform policymakers and practitioners in transportation.

Learning Objectives:

Attendees can expect to learn the following from this session:
  • Describe the opportunity that Stop-and-Go waves represent for a more sustainable transportation system
  • Illustrate the challenges in data availability and resolution (temporal and spatial)
  • Demonstrate how traffic reconstruction is a promising way to model the nuanced vehicle dynamics, from which SAG events can be identified.
  • Present uncertainty quantification as a way to evaluate the traffic reconstruction and provide more confident insights for downstream applications.