Locally kernel regression adapting with data distribution in prediction of traffic flow

Prognosis of traffic flow is a basic part of intelligent transportation research. Due to the extremely complexity of vehicular traffic, efficient models should be constructed to do accurate simulation and prediction of real traffic, such as locally kernel models. However, locally kernel regression fails when the traffic data points are sparse, and the data distribution should be considered seriously. Moreover, the spatiotemporal features of real traffic make pure locally kernel regression inapplicable. This paper proposes a locally kernel regression mechanism adapting with data distribution for the prediction of traffic flow. This mechanism is also explained by Three-Phase Traffic Theory. Experimental studies show the feasibility and efficiency of our approach.