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

arXiv: https://arxiv.org/abs/2211.10159 · HTML: https://mlsanity.com/paper/2211.10159/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 28 references that are in the map.
- First posted on arXiv 18 Nov 2022.

## Builds on (most cited first, 5 of 5)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Charging plug-in electric vehicles as a mixed-integer aggregative game](https://mlsanity.com/paper/1910.10272/) (2019)
- [Incentive-Based Electric Vehicle Charging for Managing Bottleneck Congestion](https://mlsanity.com/paper/2111.05600/) (2021)
- [Highway Traffic Control via Smart e-Mobility -- Part I: Theory](https://mlsanity.com/paper/2102.09354/) (2021)
- [A Novel Control-Oriented Cell Transmission Model Including Service Stations on Highways](https://mlsanity.com/paper/2205.15115/) (2022)

## Abstract (arXiv)

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. Microsimulators cannot be used for this task due to their computational inefficiency. We propose a genetic algorithm based on the recently proposed 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 implementing the CTMs. Finally, we examine two case studies to validate the capabilities and performance of our algorithms. In these simulations, we use real data extracted from Dutch highways.