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. 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 solve 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 analyzing 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.