An Analysis Of Traffic State Estimation Based On Particle Filter

Traffic state estimation is an important task of Intelligent Transportation System Management & Traffic Safety, also the presupposition and basis in its operation and functioning. Either traffic control or guidance system requires accurate estimations and assessments for the road traffic state in the next or even following moments. The efficiency of the decision making of the system depends on the accuracy of traffic state estimation. Only when the traffic participants are controlled and guided efficiently, the fluency of the roads and the reduction of hidden accidents can be ensured. In addition, the accurate estimation of traffic flow is a necessary condition for traffic accident detection.Currently, most literatures adopt linear approaches such as Kalman filter and extended Kalman filter techniques to estimate the traffic state estimation. These techniques can predict traffic state in relatively simple traffic condition, which has been widely used. However, traffic behaviors have higher-order nonlinearity due to the complex interaction among participants. In this case, Kalman filter has some limitations since it assumes the both the system and observation model subject to linear distributions. Moreover, it defines both the system and observation noises obey the Gaussian distribution, which leads to significant errors for the estimation result.Particle Filter (PF) is a non-linear and non-gaussian estimation technique which realizes the recursive Bayesian Filter via Monte Carlo integration simulation. From the indepth study and investigation of PF via the simulation experiment and the comparison of the PF technique with the linear EKF, we prove that PF is efficient of solving nonlinear problem.In addition, to remedy the insufficient of the traditional methods under the complex road environment, this report proposes the usage of PF in solving the traffic state estimation problem. The model which combines PF and the second-order macroscopic random traffic flow model is established on the MATLAB Platform. Experiment results based on the real world data from Beijing Loop verify that the proposed technique can estimate the traffic state parameters acutely with good adaptability.However, PF shows some limitations such as sample poverty, particle degradation, etc. To solve these problems, the thesis innovatively proposes to apply Ant Colony Algorithm (ACO) in the update process of PF and verify the ACO-PF technique with real world data. Experiment results suggest that the proposed technique is superior to the particle filter with higher accuracy and stronger robustness.