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 becaus...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.展开更多
为探究具有智能重卡(Connected and Autonomous Trucks,CAT)驾驶经验的货车驾驶员与传统货车驾驶员在网联自动驾驶车辆与人工驾驶车辆混行交通环境中的信息响应差异,采用意向调查法收集其在混行交通环境下的信息响应行为数据,并运用二元...为探究具有智能重卡(Connected and Autonomous Trucks,CAT)驾驶经验的货车驾驶员与传统货车驾驶员在网联自动驾驶车辆与人工驾驶车辆混行交通环境中的信息响应差异,采用意向调查法收集其在混行交通环境下的信息响应行为数据,并运用二元Logit模型量化影响因素。结果表明,相较于传统货车驾驶员,具有CAT驾驶经验的货车驾驶员更倾向“立即响应”,尤其在网联自动驾驶小汽车渗透率较高的环境中更为显著;而在人工驾驶卡车渗透率较高的环境下,更倾向“延迟响应”。性别、年龄、月收入、驾龄、工作强度、事故经历、过去一年驾照扣分情况、事故占道情况与车道被占数量等因素均对信息响应行为具有显著影响。展开更多
基金Project supported by the National High Technology Research and Development Program of China (Grant No. 2011AA110303)
文摘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.
文摘为探究具有智能重卡(Connected and Autonomous Trucks,CAT)驾驶经验的货车驾驶员与传统货车驾驶员在网联自动驾驶车辆与人工驾驶车辆混行交通环境中的信息响应差异,采用意向调查法收集其在混行交通环境下的信息响应行为数据,并运用二元Logit模型量化影响因素。结果表明,相较于传统货车驾驶员,具有CAT驾驶经验的货车驾驶员更倾向“立即响应”,尤其在网联自动驾驶小汽车渗透率较高的环境中更为显著;而在人工驾驶卡车渗透率较高的环境下,更倾向“延迟响应”。性别、年龄、月收入、驾龄、工作强度、事故经历、过去一年驾照扣分情况、事故占道情况与车道被占数量等因素均对信息响应行为具有显著影响。
