关键词: 智能交通, 交通状态判别, 深度学习, 交通状态, 离散化交通状态编码

Abstract:

In the intelligent transportation signal control and traffic flow guidance system, effective identification of the traffic state of the environment is a prerequisite for traffic control. Facing large scale of traffic data sets, this paper proposes a identification method of discrete traffic state based on deep learning. The system architecture include four parts of traffic state data acquisition, data description, deep learning and identification. A discrete traffic state encoding method is proposed which provide the base for deep learning of traffic state. In the phase of model training, two deep belief networks including TEDBN and TVDBN are constructed and trained for features learning with the traffic state data of binary value and continuous value. In the phase of model fine- tuning, a softmax classifier is added on the top of all high- level abstraction features, and the back- propagation algorithm is used to fine tune the parameters. At last, the method is validated on the VISSIM simulation software. The experimental results show that the method of discrete traffic state encoding can effectively express the traffic state, and the identification method based on deep learning has higher accuracy than the traditional method on the traffic state.

Key words: intelligent transportation, traffic state identification, deep learning, traffic state, discrete traffic state encoding