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Research Article | Open Access

Emissions minimization on road networks via Generic Second Order Models

Caterina Balzotti1Maya Briani2( )Benedetto Piccoli3
SISSA Scuola Internazionale Superiore di Studi Avanzati, Trieste, Italy
Istituto per le Applicazioni del Calcolo, Consiglio Nazionale delle Ricerche, Rome, Italy
Department of Mathematical Sciences, Rutgers University, Camden, USA
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Abstract

In this paper we consider the problem of estimating emissions due to vehicular traffic on complex networks, and minimizing their effect by regulating traffic at junctions. For the traffic evolution, we consider a Generic Second Order Model, which encompasses the majority of two-equations (i.e., second-order) models available in the literature, and extend it to road networks with merge and diverge junctions. The dynamics on the whole network is determined by selecting a solution to the Riemann Problems at junctions, i.e., the Cauchy problems with constant initial data on each incident road. The latter are solved by assuming the maximization of the flow and assigning a traffic distribution coefficient for outgoing roads of diverges, and a priority rule for incoming roads of merges. A general emission model is considered and its parameters are tuned to the N O x emission rate. The minimization of emissions is then formulated in terms of the traffic distribution and priority parameters, taking into account travel times. A comparison is provided between roundabouts with optimized parameters and traffic lights, which correspond to time-varying traffic priorities. Our approach can be adapted to manage traffic in complex networks in order to reduce emissions while keeping travel time at acceptable levels.

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Networks and Heterogeneous Media
Pages 694-722

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Cite this article:
Balzotti C, Briani M, Piccoli B. Emissions minimization on road networks via Generic Second Order Models. Networks and Heterogeneous Media, 2023, 18(2): 694-722. https://doi.org/10.3934/nhm.2023030

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Received: 22 July 2022
Revised: 19 May 2022
Accepted: 11 January 2023
Published: 15 June 2023
©2023 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)