  Dynamic behavior of traffic adversely affect the performance of the
prediction models in intelligent transportation applications. This study
applies Gaussian processes (GPs) to traffic speed prediction. Such predictions
can be used by various transportation applications, such as real-time route
guidance, ramp metering, congestion pricing and special events traffic
management. One-step predictions with various aggregation levels (1 to
60-minute) are tested for performance of the generated models. Univariate and
multivariate GPs are compared with several other linear, nonlinear time series,
and Grey system models using loop and Inrix probe vehicle datasets from
California, Portland, and Virginia freeways respectively. Based on the test
data samples, results are promising that GP models are able to consistently
outperform compared models with similar computational times.
