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Research Article | Open Access

Estimation of NO x and O 3 reduction by dissipating traffic waves

Maya Briani1Rosanna Manzo2Benedetto Piccoli3( )Luigi Rarità4
Consiglio Nazionale delle Ricerche, Istituto per le Applicazioni del Calcolo "Mauro Picone", Via dei Taurini, 19, Rome, 00185, Italy
Dipartimento di Scienze Politiche e della Comunicazione, University of Salerno, Via Giovanni Paolo II, 132, Fisciano (SA), 84084, Italy
Department of Mathematical Sciences, Rutgers University-Camden, 311 N. Fifth Street, Camden, New Jersey, USA
Dipartimento di Scienze Aziendali-Management & Information Systems, University of Salerno, Via Giovanni Paolo II, 132, Fisciano (SA), 84084, Italy
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Abstract

Current research directions indicate that vehicles with autonomous capabilities will increase in traffic contexts. Starting from data analyzed in R. E. Stern et al. (2018), this paper shows the benefits due to the traffic control exerted by a unique autonomous vehicle circulating on a ring track with more than 20 human-driven vehicles. Considering different traffic experiments with high stop-and-go waves and using a general microscopic model for emissions, it was first proved that emissions reduces by about 25%. Then, concentrations for pollutants at street level were found by solving numerically a system of differential equations with source terms derived from the emission model. The results outline that ozone and nitrogen oxides can decrease, depending on the analyzed experiment, by about 10% and 30%, respectively. Such findings suggest possible management strategies for traffic control, with emphasis on the environmental impact for vehicular flows.

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Networks and Heterogeneous Media
Pages 822-841

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Cite this article:
Briani M, Manzo R, Piccoli B, et al. Estimation of NO x and O 3 reduction by dissipating traffic waves. Networks and Heterogeneous Media, 2024, 19(2): 822-841. https://doi.org/10.3934/nhm.2024037

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Received: 15 May 2024
Revised: 12 August 2024
Accepted: 19 August 2024
Published: 22 August 2024
©2024 the Author(s), licensee AIMS Press.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0)