Short-Term Traffic Prediction on Different Types of Roads with Genetically Designed Regression and Time Delay Neural Network Models

Abstract

Research for advanced traveler information systems (ATIS) has been focused on urban roads. However, research for short-term traffic prediction on all categories of highways is needed, as highway agencies expect to implement intelligent transportation systems across their jurisdictions. In this study, genetic algorithms were used to design time delay neural network (TDNN) models as well as locally weighted regression models to predict short-term traffic for six rural roads from Alberta, Canada. These roads are from various trip-pattern groups and functional classes. Refined TDNN models developed in this study can limit most average errors less than 10% for all study roads. Refined regression models show even higher accuracy. Average errors for the refined regression models are less than 2% for roads with stable patterns. Even for roads with unstable patterns, average errors are below 4%, and the 95th percentile errors are less than 7%. It is believed that such accurate predictions would be useful for highway agencies to implement statewide ATIS.