Aljamal Mohammad A, Abdelghaffar Hossam M, Rakha Hesham A
Charles E. Via, Jr. Department of Civil and Environmental Engineering, Center for Sustainable Mobility, Virginia Tech Transportation Institute, Virginia Tech, Blacksburg, VA 24061, USA.
Department of Computer Engineering and Systems, Engineering Faculty, Mansoura University, Mansoura 35516, Egypt.
Sensors (Basel). 2020 Jul 22;20(15):4066. doi: 10.3390/s20154066.
The paper presents a nonlinear filtering approach to estimate the traffic stream density on signalized approaches based solely on connected vehicle (CV) data. Specifically, a particle filter (PF) is developed to produce reliable traffic density estimates using CV travel-time measurements. Traffic flow continuity is used to derive the state equation, whereas the measurement equation is derived from the hydrodynamic traffic flow relationship. Subsequently, the PF filtering approach is compared to linear estimation approaches; namely, a Kalman filter (KF) and an adaptive KF (AKF). Simulated data are used to evaluate the performance of the three estimation techniques on a signalized approach experiencing oversaturated conditions. Results demonstrate that the three techniques produce accurate estimates-with the KF, surprisingly, being the most accurate of the three techniques. A sensitivity of the estimation techniques to various factors including the CV level of market penetration, the initial conditions, and the number of particles in the PF is also presented. As expected, the study demonstrates that the accuracy of the PF estimation increases as the number of particles increases. Furthermore, the accuracy of the density estimate increases as the level of CV market penetration increases. The results indicate that the KF is least sensitive to the initial vehicle count estimate, while the PF is most sensitive to the initial condition. In conclusion, the study demonstrates that a simple linear estimation approach is best suited for the proposed application.
本文提出了一种非线性滤波方法，仅基于车联网（CV）数据来估计信号控制进口道上的交通流密度。具体而言，开发了一种粒子滤波器（PF），利用CV行程时间测量值来产生可靠的交通密度估计值。利用交通流连续性推导状态方程，而测量方程则从交通流动力学关系中推导得出。随后，将PF滤波方法与线性估计方法进行比较，即卡尔曼滤波器（KF）和自适应KF（AKF）。使用模拟数据评估这三种估计技术在经历过饱和状况的信号控制进口道上的性能。结果表明，这三种技术都能产生准确的估计值，令人惊讶的是，KF是这三种技术中最准确的。还给出了估计技术对各种因素的敏感性，包括CV市场渗透率、初始条件以及PF中的粒子数量。正如预期的那样，研究表明PF估计的准确性随着粒子数量的增加而提高。此外，密度估计的准确性随着CV市场渗透率的提高而增加。结果表明，KF对初始车辆计数估计最不敏感，而PF对初始条件最敏感。总之，研究表明简单的线性估计方法最适合所提出的应用。
