Urban Traffic Congestion Status Automatically Determine The Method Of Study

With the supports of the projects "Study on Multiple Problems in Municipal Transportation System" of the Natural Sciences Foundation of China, and "Study on Guiyang Road Traffic Database Construction and Network Modeling" of Guiyang Science and Technology Foundation, this thesis focuses on algorithms for automatic identification of traffic congestion.A method of traffic congestion identification based on Bayes classifier theory is presented through analysis of municipal traffic congestion characteristic. Whether traffic congestion occurs or not is considered as a special classification problem. If the branch road of city is not affected by the signal light, the traffic situation is divided into tow parts: congestion and unimpeded. Using data associated with the traffic parameter for congestion and non congestion, an incremental Bayes classifier is trained to detect whether traffic congestion occur or not. Experimental results based on microcosmic traffic simulation indicate that this method is not only feasible but also effective.To reduce the effects of noise in training data set on the capability of classifiers, an incremental optimize algorithm is proposed, in which primitive training set is firstly spitted into two parts, and the first part is trained to look for the superior subset from the second part. Secondly, the superior subset is trained to obtain a new superior subset from the first part, and iterate this process in turn to obtain the most superior training subset of primitive training set. This method needs not any set threshold, and be convergence automatically. Besides, the optimized process fully uses the sample information. Experiments show that the classification precision can be enhanced by incremental optimize algorithm.Finally, this thesis designed the overall framework of the municipal road traffic automatic identification system and the incremental Bayes traffic congestion identification subsystem, and presented some proposals for the function module design and the database construction.