Traffic Flow Forecasting Based on Pattern Recognition to Overcome Memoryless Property

Abstract

A variety of methods and techniques have been developed to forecast traffic flow. Current nearest neighbor non-parametric traffic flow forecasting models treat the dynamic evolution of traffic flows at a given state as a memoryless process; the current state of traffic flow entirely determines the future state of traffic flow, with no dependence on the past sequences of traffic flow patterns that produced the current state. Since traffic flow is not completely random in nature, there should be some patterns in which the past traffic flow repeats itself. In this paper, we proposed a pattern recognition technique, which enables us to consider the past sequences of traffic flow patterns to predict the future state. It was found that the pattern recognition model is capable of predicting the future state of traffic flow reasonably well compared with the k-nearest neighbor non-parametric regression model.