基于序贯蒙特卡洛算法的交通流事件重构

针对交通数据重构应用性差、缺乏对交通事件重构的研究等问题,结合交通流非线性非高斯的特点,提出一个基于序贯蒙特卡洛方法的交通流堵塞事件重构模型。该模型不断同化道路上的传感器数据,使仿真中的交通状态不断逼近真实路况,通过分析仿真数据以探测真实路网中存在的堵塞事件。模型能够对探测到的堵塞进行多粒子模拟来实现对真实道路上堵塞事件的重构。实验结果表明,该模型能够推测并重构出道路上的堵塞事件,对堵塞起始位置重构的平均误差为17m,对堵塞范围重构的平均覆盖率为82%。

Event reconstruction for traffic flow simulation based on sequential Monte Carlo

Combining with the nonlinear and non-Gaussian characteristics of the traffic flow, we propose a traffic flow congestion event reconstruction framework based on the Sequential Monte Carlo (SMC) to deal with the congestion reconstruction problem. The simulation's states can get close to the real scene continuously when the data assimilation model assimilates the real-time sensor data constantly. The congestion event in real scene can be estimated based on the simulation data. Thus, the simulation model can simulate the congestion via different particle simulations and finally reconstruct the congestion event. Experimental results show that the framework can detect and reconstruct the congestion event on the real road network; the average error of the start position is 17m, while the average coverage rate of the congestion range is 82%.