Emission reduction potential of freeway stop-and-go wave smoothing

TL;DR

This study quantifies the potential emission reductions from freeway stop-and-go wave smoothing by reconstructing feasible traffic trajectories and applying emission models, showing significant potential benefits in real-world traffic conditions.

Contribution

It introduces a counterfactual wave smoothing benchmark using quadratic programming and estimates emission reduction potential from empirical traffic data.

Findings

Average CO2 reductions of 7.92% to 12.04% across lanes

Concurrent reductions of 14.30% to 28.91% CO, 23.15% to 29.42% HC, and 24.37% to 30.98% NOx

Quantifies trade-offs between gap size and emission benefits

Abstract

Real-world potential of stop-and-go wave smoothing at scale remains largely unquantified. Smoothing freeway traffic waves requires creating a gap so the wave can dissipate, but the gap suggested is often too large and impractical. We propose a counterfactual wave smoothing benchmark that reconstructs a smooth and feasible trajectory from each empirical trajectory by solving a quadratic program with fixed boundary conditions and a maximum allowable gap constraint. We estimate the emission reduction potential from trajectories using the MOVES model. Applying the framework to nine weeks of weekday peak traffic data, featuring rich day-to-day stop-and-go wave dynamics, from the I-24 MOTION testbed, we find meaningful reduction potential under a 0.1-mile maximum gap: average CO2 reductions of 7.92% to 12.04% across lanes, with concurrent reductions of 14.30% to 28.91% CO, 23.15% to 29.42% HC, and 24.37% to 30.98% NOx. Our analysis also quantifies the trade-off between maximum allowable gap opening and emissions benefits.