Using Model Predictive Control To Reduce Traffic Emissions on Urban Freeways

TL;DR

This paper introduces an MPC-based framework that optimizes variable speed limits on urban freeways to reduce traffic congestion and emissions, incorporating realistic driver behavior and a traffic flow model.

Contribution

It presents a novel MPC approach with a bounded-acceleration traffic model to effectively reduce emissions and manage congestion on freeway corridors.

Findings

Effective reduction in emissions demonstrated in simulations

Improved traffic flow and congestion management

Balanced optimization of travel time and emissions

Abstract

Urban traffic congestion significantly impacts regional air quality and contributes substantially to pollutant emissions. Suburban freeway corridors are a major source of traffic-related emissions, particularly nitrogen oxides (NOx) and carbon dioxide (CO2). This paper proposes a Model Predictive Control (MPC) framework aimed at emission reduction on peripheral freeway corridors. Emission rates on freeways exhibit high sensitivity to speed fluctuations and congestion recovery processes. To address this relationship, we develop and analyze a bounded-acceleration continuum traffic flow model. By introducing an upper limit on vehicle acceleration capabilities, we enhance behavioral realism through the incorporation of driver responses to congestion, which is widely recognized as a main cause of the important capacity drop phenomenon. Our approach implements dynamically optimized variable speed limits (VSLs) at strategic corridor locations, balancing the dual objectives of minimizing both travel time and emissions as quantified by the COPERT V [1] model. Numerical simulations demonstrate that this framework effectively manages congestion and reduces emissions across various traffic demand scenarios.