Particle Filter (PF) is a data assimilation method to solve recursive state estimation problem which does not depend on the assumption of Gaussian noise, and is able to be applied for various systems even with non-linear and non-Gaussian noise. However, while applying PF in dynamic systems, PF undergoes particle degeneracy, sample impoverishment, and problems of high computational complexity. Rapidly developing sensing technologies are providing highly convenient availability of real-time big traffic data from the system under study like never before. Moreover, some sensors can even receive control commands to adjust their monitoring parameters. To address these problems, a bidirectional dynamic data-driven improvement framework for PF (B3DPF) is proposed. The B3DPF enhances feedback between the simulation model and the big traffic data collected by the sensors, which means the execution strategies (sensor data management, parameters used in the weight computation, resampling) of B3DPF can be optimized based on the simulation results and the types and dimensions of traffic data injected into B3DPF can be adjusted dynamically. The first experiment indicates that the B3DPF overcomes particle degeneracy and sample impoverishment problems and accurately estimates the state at a faster speed than the normal PF. More importantly, the new method has higher accuracy for multidimensional random systems. In the rest of experiments, the proposed framework is applied to estimate the traffic state on a real road network and obtains satisfactory results. More experiments can be designed to validate the universal properties of B3DPF.
According to the nonlinear and non-Gaussian characteristics of the traffic flow, we propose a SMC based traffic flow congestion event reconstruction framework based on traffic flow signals. The simulation states can get close to the real scene continuously along with the data assimilation model assimilates the real-time traffic signals constantly. The congestion event in real scene can be estimated based on the simulation data. Thus, we can estimate the congestion in different particles and finally reconstruct the congestion event. This framework can evaluate the current roads' states based on the reconstruction results, and then the range and the start position of the congestion can be determined. Related experimental results are presented and analyzed.
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