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.

翻译：本文分析高速公路上服务站的存在如何影响交通堵塞。 我们集中研究优化设计服务站的问题,以便在交通全面堵塞和高峰交通减少方面产生有益影响。 微缩模拟器由于计算效率低而无法用于这项任务。 我们根据最近提出的计算机数据仪提出遗传算法,有效地描述服务站的动态。 然后, 我们利用算法来训练一个神经网络,能够解决同样的问题,避免执行计算机数据仪。 最后, 我们研究两个案例研究,以验证我们算法的能力和性能。 在这些模拟中,我们使用从荷兰高速公路上提取的真实数据。