论文标题
基于学习的交通状态重建使用探测器
Learning-based Traffic State Reconstruction using Probe Vehicles
论文作者
论文摘要
本文调查了使用来自探测器的嘈杂测量值的基于模型的神经网络用于交通重建问题。假定交通状态仅是密度，由部分微分方程建模。在这种情况下，存在各种方法来重建密度。但是，它们都没有很好地表现出噪音，很少处理拉格朗日测量。本文介绍了一种可以将识别，重建，预测和噪声拒绝过程减少到单个优化问题中的方法。基于宏观或微观模型的数值模拟显示出适度的计算负担的良好性能。
This article investigates the use of a model-based neural-network for the traffic reconstruction problem using noisy measurements coming from probe vehicles. The traffic state is assumed to be the density only, modeled by a partial differential equation. There exist various methods for reconstructing the density in that case. However, none of them perform well with noise and very few deal with lagrangian measurements. This paper introduces a method that can reduce the processes of identification, reconstruction, prediction, and noise rejection into a single optimization problem. Numerical simulations, based either on a macroscopic or a microscopic model, show good performance for a moderate computational burden.
