A genetic resampling particle filter for freeway traffic-state estimation
Bi Jun(毕军)†, Guan Wei(关伟), and Qi Long-Tao(齐龙涛)
Key Laboratory for Urban Transportation Complex Systems Theory and Technologyof the Ministry of Education, Beijing Jiaotong University, Beijing 100044, China
Abstract On-line estimation of the state of traffic based on data sampled by electronic detectors is important for intelligent traffic management and control. Because a nonlinear feature exists in the traffic state, and because particle filters have good characteristics when it comes to solving the nonlinear problem, a genetic resampling particle filter is proposed to estimate the state of freeway traffic. In this paper, a freeway section of the northern third ring road in the city of Beijing in China is considered as the experimental object. By analysing the traffic-state characteristics of the freeway, the traffic is modeled based on the second-order validated macroscopic traffic flow model. In order to solve the particle degeneration issue in the performance of the particle filter, a genetic mechanism is introduced into the resampling process. The realization of a genetic particle filter for freeway traffic-state estimation is discussed in detail, and the filter estimation performance is validated and evaluated by the achieved experimental data.
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