Refining Genetically Designed Models for Improved Traffic Prediction on Rural Roads

Abstract

Research into advanced traveler information systems (ATIS) for rural roads is limited. However, highway agencies expect to implement intelligent transportation systems (ITS) in both urban and rural areas. In this paper, genetic algorithms (GAs) are used to design both time delay neural network (TDNN) models as well as locally weighted regression (LWR) models to predict short-term traffic for two rural roads in Alberta, Canada. A top-down refinement was used to study the interactions between modeling techniques and underlying data sets for obtaining highly accurate models. It is found that LWR models achieve faster accuracy improvement than TDNN models over the refinement process. Compared with previous research, the models proposed here show higher accuracy. The average errors for the best LWR models obtained through the model-refining process are less than 2% in most cases. For refined TDNN models, the average errors are usually less than 6--7%. The resulting models indicate a level of high robustness over different types of roads, and thus may be considered desirable for real-world statewide ITS implementations.