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

被引:1
作者
Cenedese, Carlo [1 ]
Cucuzzella, Michele [2 ]
Ramusino, Adriano Cotta [2 ]
Spalenza, Davide [2 ]
Lygeros, John [1 ]
Ferrara, Antonella [2 ]
机构
[1] Swiss Fed Inst Technol, Dept Informat Technol & Elect Engn, Zurich, Switzerland
[2] Univ Pavia, Dept Elect Comp & Biomed Engn, Pavia, Italy
来源
IFAC PAPERSONLINE | 2023年 / 56卷 / 02期
基金
瑞士国家科学基金会;
关键词
genetic algorithm; neural network; traffic control management; service station; design; smart mobility; ELECTRIC VEHICLES;
D O I
10.1016/j.ifacol.2023.10.1849
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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 (CTMs), 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.
引用
收藏
页码:1528 / 1533
页数:6
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