Evaluation of $\mathrm{NO_x}$ emissions and ozone production due to vehicular traffic via second-order models

TL;DR

This paper models vehicular traffic and chemical reactions to estimate ozone production from NOx emissions, analyzing traffic scenarios and traffic light timing effects.

Contribution

It introduces a coupled second-order traffic and chemical reaction model tuned on real data to assess ozone impact from traffic emissions.

Findings

Longer green traffic light phases reduce NOx emissions.

Vehicle restarts increase NOx emissions.

Traffic light timing significantly affects ozone production.

Abstract

The societal impact of traffic is a long-standing and complex problem. We focus on the estimation of ozone production due to vehicular traffic. For this, we couple a system of conservation laws for vehicular traffic, an emission model, and a system of partial differential equations for the main reactions leading to ozone production and diffusion. The second-order model for traffic is obtained by choosing a special velocity function for a Collapsed Generalized Aw-Rascle-Zhang model and is tuned on NGSIM data. On the other side, the system of partial differential equations describes the main chemical reactions of $\mathrm{NO_x}$ gases with a source term provided by a general emission model applied to the output of the traffic model. We analyze the ozone impact of various traffic scenarios and describe the effect of traffic light timing. The numerical tests show the negative effect of vehicles restarts on $\mathrm{NO_x}$ emissions, suggesting to increase the length of the green phase of traffic lights to reduce them.