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

TL;DR

This paper presents a genetic algorithm and neural network approach to optimally design highway service stations for reducing traffic congestion, validated with real Dutch highway data.

Contribution

It introduces a novel genetic algorithm based on CTMs and trains a neural network to efficiently optimize service station placement for traffic mitigation.

Findings

The algorithms effectively reduce traffic congestion in case studies.

Neural network approach speeds up the optimization process.

Validated with real-world Dutch highway data.

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. 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.