Wang Song, Xie Xu, Ju Rusheng
College of Systems Engineering, National University of Defense Technology, Changsha 410073, China.
Entropy (Basel). 2019 Apr 3;21(4):358. doi: 10.3390/e21040358.
Traffic conditions can be more accurately estimated using data assimilation techniques since these methods incorporate an imperfect traffic simulation model with the (partial) noisy measurement data. In this paper, we propose a data assimilation framework for vehicle density estimation on urban traffic networks. To compromise between computational efficiency and estimation accuracy, a mesoscopic traffic simulation model (we choose the platoon based model) is employed in this framework. Vehicle passages from loop detectors are considered as the measurement data which contain errors, such as missed and false detections. Due to the nonlinear and non-Gaussian nature of the problem, particle filters are adopted to carry out the state estimation, since this method does not have any restrictions on the model dynamics and error assumptions. Simulation experiments are carried out to test the proposed data assimilation framework, and the results show that the proposed framework can provide good vehicle density estimation on relatively large urban traffic networks under moderate sensor quality. The sensitivity analysis proves that the proposed framework is robust to errors both in the model and in the measurements.
使用数据同化技术可以更准确地估计交通状况，因为这些方法将不完美的交通模拟模型与（部分）有噪声的测量数据相结合。在本文中，我们提出了一种用于城市交通网络车辆密度估计的数据同化框架。为了在计算效率和估计精度之间进行折衷，该框架采用了一种中观交通模拟模型（我们选择基于车队的模型）。来自环形检测器的车辆通行情况被视为包含误差（如漏检和误检）的测量数据。由于该问题具有非线性和非高斯特性，因此采用粒子滤波器进行状态估计，因为该方法对模型动态和误差假设没有任何限制。进行了仿真实验来测试所提出的数据同化框架，结果表明，在中等传感器质量下，所提出的框架能够在相对较大的城市交通网络上提供良好的车辆密度估计。敏感性分析证明，所提出的框架对模型和测量中的误差具有鲁棒性。
