Gray Markov Model in the Intersection of Short-Term Traffic Flow Prediction

Abstract

With the rapid development of urbanization, the number of vehicles increased faster than urban roads in a city. The contradiction between supply and demand of urban transportation is increasingly sharp. Intelligent transportation system is an internationally recognized settlement of the best way to solve city traffic congestion. Traffic flow prediction, a key technology of the intelligent system, is the premise and key to traffic flow inducing and controlling. The intersection is the most vulnerable and most likely to appear problem in urban road traffic. Research on intelligent traffic conditions of intersection traffic characteristics, improve the capacity of the intersection is the key and main task of solving the city traffic problem. Short term traffic prediction model of previous research is still not perfect, low computational accuracy, need a large amount of data, the prediction model of complex, operation time is long, and so on. The grey prediction model has the advantages of simple model, less data is needed and short operation time. In this paper, based on the grey prediction model of these advantages combined with the Markov model, to establish a new method of grey model to forecast the short-term traffic flow prediction, and applied to the intersection of overcoming the insufficiency of the two kinds of prediction method, improve the prediction accuracy, carry on the forecast analysis.