Research on Prediction of Traffic Congestion State

• Main Author: Zhao Jun
• Format: Article
• Language: English
• Published: EDP Sciences 2015-01-01
• Series: MATEC Web of Conferences
• Subjects: intelligent transportation systems; data mining; fuzzy clustering; traffic
• Online Access: http://dx.doi.org/10.1051/matecconf/20152201059

This method of prediction using the data mining to analyze huge amounts of data as a preferred tool has been widely used in various fields. In the midst of it, the routine traffic data exists in a large number of isolated data in real time without establishing relationships with other data, and detects the amount of data which is greater than that at present. The usage of these data which is relatively shallow requires an in-depth analysis of its data model. Therefore, this paper uses a fuzzy clustering analysis method of feature points to study the traffic flow, uses a Markov decision chain model to study traffic jams, uses quantitative sample points based on the information entropy to calculate traffic flow trends and uses a heuristic prediction model to predict the road con-gestion. Through the simulation experiment which verifies the correctness of the model, this research is to advance the development of the road and to provide a basis for a dredging plan.