关键词: 智能交通, 交通流预测, 灰色系统, 马尔科夫链

Abstract: In order to accurately describe the spatial-temporal evolution of the traffic flow and improve the accuracy of short-term traffic flow prediction, this paper builds the prediction model based on GM（1，N）-Markov chain, by fusing the spatial-temporal traffic information. Firstly, the prediction section and the association sections are taken as a grey system, and the grey relational analysis of the sections and setting the lowest threshold of grey correlation degree can mine the spatial information and reduce the ineffective information; secondly, the prediction section and strong correlation sections are analyzed systematically and then predicted by GM（1，N） model. In view of the failure caused by the randomness of sequence, the Markov chain is introduced to modify the forecasting model; finally, VISSIM is used for simulating, the simulation respectively taken 2 min, 5 min, 10 min as a time interval, the predicted average relative error is 9.30%, 5.95% and 3.20% respectively, the model accuracy is optimal, and this model is proven to be effective.

Key words: intelligent transportation, traffic flow forecasting, grey system, Markov chain