High-Order Gaussian Process Dynamical Models for Traffic Flow Prediction

Abstract

Traffic flow prediction, which predicts the future flow using historic flows, is an important task in intelligent transportation systems (ITS). Efficient and accurate models for traffic flow prediction greatly contribute to the development of ITS. In this paper, we adopt the Gaussian process dynamical model (GPDM) to a fourth-order GPDM, which is more suitable for modeling traffic flow data. Specifically, the latent variables in the fourth-order GPDM is a fourth-order Markov Gaussian process, and the weighted $k$ - NN is incorporated in the model to predict latent variables for efficient prediction. After training the model, the future flow is estimated by the average of the results predicted by the fourth-order GPDM and $k$ - NN. Compared with other popular methods, the proposed method performs best and yields significant improvements of prediction performance.