论文标题
在不同聚合级别的流量速度预测的高斯流程
Gaussian Processes for Traffic Speed Prediction at Different Aggregation Levels
论文作者
论文摘要
流量的动态行为不利影响智能运输应用中预测模型的性能。这项研究将高斯流程（GPS）应用于交通速度预测。可以通过各种运输应用程序使用此类预测，例如实时路线指南，坡道计量，拥堵定价和特殊活动交通管理。测试了具有各种聚合水平（1至60分钟）的一步预测，以进行生成模型的性能。将单变量和多变量GPS与其他几个线性，非线性时间序列和灰色系统模型进行了比较，分别来自加利福尼亚州，波特兰和弗吉尼亚州高速公路的LOOP和INRIX Probe车辆数据集。基于测试数据样本，结果有望使GP模型能够始终如一地超过与模型与相似的计算时间的比较。
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.
