基于分布式车辆速度检测信息的城市快速路交通状态估计

Traffic State Estimation of Urban Freeway Traffic Based on Distributed Speed Detecting Information Networks

移动通讯与便携式交通检测技术的进步,使得路网交通信息采集更加准确和便捷,也使得估计道路任意位置、任何时刻处的交通参数成为可能。作为一个典型的分布式参数系统,交通流动态由道路交通状态与边界上交通需求-供给关系共同确定。针对城市快速路,本文通过车辆GPS速度检测参数构建观测网络,利用扩展卡尔曼滤波同步估计子路段的交通密度和边界流量,然后采用一致性平均算法融合上下游子路段的边界流量,更新交通参数估计,实现城市快速路网交通状态的实时分布式估计。最后利用Pe MS和Mobile Century交通数据验证了该方法的有效性。

With the rapid development of mobile communication and portable traffic detection technologies, the roadnet traffic information could be collected more accurately and quickly. These sensing technologies have the potential for collecting data at any time and positions. As a typical distributed parameter system, the freeway traffic dynamic state is determined by both the current system states and the boundary traffic demand-supply. For the urban highway and using the consensus-based Decentralized Extended Kalman Filtering, the authors estimated the real-time traffic density and the boundary flow of the freeway traffic with the distributed speed detector networks organized at any interested positions. Second, we adopted the consensus average algorithm to fuse the boundary flow between the adjacent links, to connect and update the estimation of traffic parameters of the local links. Two key procedures were developed to perform thereal-time distributed estimation of traffic state for large-scale freeway networks. Simulation and experimental results validated the feasibility and superiority of this method.