论文标题
在有限的干扰下，基于不对称的细胞传输模型，斜坡连接的稳健交通密度估计
Asymmetric Cell Transmission Model-Based, Ramp-Connected Robust Traffic Density Estimation under Bounded Disturbances
论文作者
论文摘要
在现代运输系统中，交通拥堵是不可避免的。为了最大程度地减少拥堵造成的损失，已经制定了各种控制策略，其中大多数依赖于观察实时交通状况。由于老式的交通传感器有限，交通密度估计对于获得网络范围的可观察性非常有帮助。本文首先解决了这个问题，它提出了一个用于拉伸高速公路的交通模型，该高速公路具有基于非对称电池传输模型（ACTM）的多个坡道。其次，基于ACTM包含的非线性的假设是Lipschitz，提出了用于执行交通密度估计的强大动态观察者框架。数值测试结果表明，观察者在估计具有嘈杂测量值的交通密度方面产生足够的性能，同时在执行实时估计的情况下计算更快。
In modern transportation systems, traffic congestion is inevitable. To minimize the loss caused by congestion, various control strategies have been developed most of which rely on observing real-time traffic conditions. As vintage traffic sensors are limited, traffic density estimation is very helpful for gaining network-wide observability. This paper deals with this problem by first, presenting a traffic model for stretched highway having multiple ramps built based on asymmetric cell transmission model (ACTM). Second, based on the assumption that the encompassed nonlinearity of the ACTM is Lipschitz, a robust dynamic observer framework for performing traffic density estimation is proposed. Numerical test results show that the observer yields a sufficient performance in estimating traffic densities having noisy measurements, while being computationally faster the Unscented Kalman Filter in performing real-time estimation.
