Short-term freeway traffic parameter prediction: Application of grey system theory models

Intelligent transportation systems applications require accurate and robust prediction of traffic parameters such as speed, travel time, and flow. However, traffic exhibits sudden shifts due to various factors such as weather, accidents, driving characteristics, and demand surges, which adversely affect the performance of the prediction models. This paper studies possible applications and accuracy levels of three Grey System theory models for short-term traffic speed and travel time predictions: first order single variable Grey model (GM(1,1)), GM(1,1) with Fourier error corrections (EFGM), and the Grey Verhulst model with Fourier error corrections (EFGVM). Grey models are tested on datasets from California and Virginia. They are compared to nonlinear time series models. Grey models are found to be simple, adaptive, able to deal better with abrupt parameter changes, and not requiring many data points for prediction updates. Based on the sample data used, Grey models consistently demonstrate lower prediction errors over all the time series improving the accuracy on average about 50% in Root Mean Squared Errors and Mean Absolute Percent Errors.