Markov chain and neural network based traffic congestion state combined prediction method

The invention relates to a Markov chain and neural network based traffic congestion state combined prediction method. The Markov chain and neural network based traffic congestion state combined prediction method comprises the following steps of 1 adopting a similar-PageRank Markov chain method to perform traffic congestion state prediction so as to obtain a first prediction result, 2 adopting a quantum multi-agent algorithm optimized back-propagating (BP) neural network method to perform traffic congestion state prediction so as to obtain a second prediction result, 3 obtaining the weight of the first prediction result and the weight of the second prediction result based on information entropy, 4 obtaining a final prediction result according to the first prediction result, the second prediction result and the corresponding weights. Compared with the prior art, the Markov chain and neural network based traffic congestion state combined prediction method has the advantages of being good in prediction real-timeliness, high in accuracy, good in extension and the like.