Traffic flow forecasting: overcoming memoryless property in nearest neighbor non-parametric regression

Taehyung Kim, Hyoungsoo Kim, and David J. Lovell

Proceedings of the 8th IEEE Intelligent Transportation Systems Conference (ITSC), Vienna, Austria, 2005.

ABSTRACT

Short term traffic flow forecasting has played a key role in proactive and dynamic traffic control systems. 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; i.e., 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 (in existing nearest neighbor non-parametric models, the state includes only instantaneous conditions, not historic ones). Of course, 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 have 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. We hope that this paper is a good platform for the development of more effective nearest neighbor non-parametric regression models.