论文标题
通过遗传算法和神经网络缓解交通的最佳服务站设计
Optimal service station design for traffic mitigation via genetic algorithm and neural network
论文作者
论文摘要
本文分析了高速公路上的服务站的存在如何影响交通拥堵。我们专注于最佳设计服务站以在总交通拥堵和峰值交通减少方面实现有益效果的问题。由于其计算效率低下，因此无法将微型模拟器用于此任务。我们根据最近提出的CTM提出了一种遗传算法，该算法有效地描述了服务站的动力学。然后，我们利用算法来训练能够解决相同问题的神经网络，避免实施CTM。最后，我们研究了两个案例研究，以验证算法的能力和性能。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。
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.
