基于动态贝叶斯网络的交通流状态辨识方法

为准确地对交通流状态进行辨识，进而支持交通流实时诱导系统有效运行，结合速度、流量与车道占有率3种交通流参数，将贝叶斯网络用于交通流状态辨识，提出了基于动态贝叶斯网络的交通流状态辨识方法. 利用英国南安普敦市的实际数据对上述方法进行了仿真验证. 验证结果表明，利用动态贝叶斯交通流状态辨识方法可以更加准确地判别出交通流所处的运行状态，这为智能交通系统，特别是交通流实时诱导系统，提供了一定的理论支持. 

Research on Traffic Flow States Identification Method Based on Dynamic Bayesian Networks

In order to accurately identify states of traffic flow, and support real-time traffic flow guidance system, the traffic flow states identification method is put forward based on dynamic Bayesian networks, combining three kinds of traffic flow parameters (speed, volume and occupancy). The method is validated through simulation experiments making use of the date of the city of Southampton, UK. The results show that the traffic flow identification method based on dynamic Bayesian networks can determine more accurately states of traffic flow. It provides a theoretical support for intelligent transportation systems, particularly real-time traffic flow guidance system.