Gaussian processes for short-term traffic volume forecasting

Yuanchang Xie*, Kaiguang Zhao, Ying Sun, Dawei Chen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

88 Scopus citations


The accurate modeling and forecasting of traffic flow data such as volume and travel time are critical to intelligent transportation systems. Many forecasting models have been developed for this purpose since the 1970s. Recently kernel-based machine learning methods such as support vector machines (SVMs) have gained special attention in traffic flow modeling and other time series analyses because of their outstanding generalization capability and superior nonlinear approximation. In this study, a novel kernel-based machine learning method, the Gaussian processes (GPs) model, was proposed to perform short-term traffic flow forecasting. This GP model was evaluated and compared with SVMs and autoregressive integrated moving average (ARIMA) models based on four sets of traffic volume data collected from three interstate highways in Seattle, Washington. The comparative results showed that the GP and SVM models consistently outperformed the ARIMA model. This study also showed that because the GP model is formulated in a full Bayesian framework, it can allow for explicit probabilistic interpretation of forecasting outputs. This capacity gives the GP an advantage over SVMs to model and forecast traffic flow.

Original languageEnglish (US)
Pages (from-to)69-78
Number of pages10
JournalTransportation Research Record
Issue number2165
StatePublished - Jan 12 2010
Externally publishedYes

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Mechanical Engineering


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