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.

中文翻译：

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不同聚合级别的交通速度预测的高斯过程

交通的动态行为会对智能交通应用中的预测模型的性能产生不利影响。本研究将高斯过程（GPs）应用于交通速度预测。此类预测可以由各种运输应用程序使用，例如实时路线引导，匝道计量，拥堵定价和特殊事件交通管理。测试了具有各种聚合级别（1至60分钟）的单步预测的生成模型的性能。使用分别来自加利福尼亚州，波特兰市和弗吉尼亚州高速公路的环路和Inrix探测车辆数据集，将单变量GP和多变量GP与其他几个线性，非线性时间序列和Gray系统模型进行了比较。根据测试数据样本，