Optimal service station design for traffic mitigation via genetic algorithm and neural network

Journal Article (2023)
Author(s)

Carlo Cenedese (ETH Zürich)

Michele Cucuzzella (Pavia University)

Adriano Cotta Ramusino (Pavia University)

Davide Spalenza (Pavia University)

John Lygeros (ETH Zürich)

Antonella Ferrara (ETH Zürich)

Affiliation
External organisation
DOI related publication
https://doi.org/10.1016/j.ifacol.2023.10.1849
More Info
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Publication Year
2023
Language
English
Affiliation
External organisation
Issue number
2
Volume number
56
Pages (from-to)
1528-1533

Abstract

This paper analyzes how the presence of service stations on highways affects traffic congestion. We focus on the problem of optimally designing a service station to achieve beneficial effects in terms of total traffic congestion and peak traffic reduction. We propose a genetic algorithm based on the recently proposed Cell Transmission Model with service station (CTM-s), that efficiently describes the dynamics of a service station. Then, we leverage the algorithm to train a neural network capable of solving the same problem, avoiding to implement the CTM-s. Finally, we validate the performance of our algorithms by using real data from Dutch highways.

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