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Dynamic behavior of traffic adversely affect the performance of theprediction models in intelligent transportation applications. This studyapplies Gaussian processes (GPs) to traffic speed prediction. Such predictionscan be used by various transportation applications, such as real-time routeguidance, ramp metering, congestion pricing and special events trafficmanagement. One-step predictions with various aggregation levels (1 to60-minute) are tested for performance of the generated models. Univariate andmultivariate GPs are compared with several other linear, nonlinear time series,and Grey system models using loop and Inrix probe vehicle datasets fromCalifornia, Portland, and Virginia freeways respectively. Based on the testdata samples, results are promising that GP models are able to consistentlyoutperform compared models with similar computational times.

Gaussian Processes for Traffic Speed Prediction at Different Aggregation Levels