Meeting Real–Time Traffic Flow Forecasting Requirements with Imprecise Computations

This article explores the ability of imprecise computations to address real–time computational requirements in infrastructure control and management systems. The research in this area focuses on the development of nonparametric regression as a means to forecast traffic flow rates for transportation management systems. Nonparametric regression is a forecasting technique based on nearest neighbor searching, in which forecasts are derived from past observations that are similar to current conditions. A key concern regarding nonparametric regression is the significant time required to search for nearest neighbors in large databases. The results presented in this article indicate that approximate nearest neighbors, which are imprecise computations as applied to nonparametric regression, may be used to adequately speed the execution time of nonparametric regression, with acceptable degradations in forecast accuracy. The article concludes with a demonstration of the use of genetic algorithms as a design aid for real–time algorithms employing imprecise computations.