A Bayesian clustering ensemble Gaussian process model for network-wide traffic flow clustering and prediction

Abstract

Traffic flow prediction is an essential component in intelligent transportation systems. Recently, there has been a notable trend in applying machine learning models, especially deep learning, for network-wide traffic prediction. However, existing studies have limitations on model interpretability, model generalization, and over-reliance on image data processing or fine-designed deep learning structures for extracting traffic attributes. This paper attempts to tackle these limitations by proposing a Bayesian clustering ensemble Gaussian process (BCEGP) model for network-wide traffic flow clustering and prediction. The model utilizes a subset-based Dirichlet process mixture (SDPM) model to conduct a hard clustering among input data; then, within each cluster, it adopts the Gaussian Process (GP) to learn the probability relationship between inputs and outputs. During the prediction phase, the model conducts a soft clustering of the input as weights, and makes predictions via a weighted average of GPs’ outputs. The merits of the BCEGP model include: (a) data with similar spatial–temporal patterns are clustered, which helps understand traffic dynamics in a non-Euclidean and non-graphical manner that enhances information extracting for model development; (b) GPs provide analytically trackable functions/gradients of predicted traffic flows with features and reveal variances of predicted traffic flow, enhancing model applicability and interpretability to some extent; (c) the model incorporates an ensemble learning framework that achieves great generalization performance as good as deep learning models; (d) the subset-based clustering and cluster-based GP learning are conducted parallelly, and thus vastly accelerate the training efficiency compared with conventional GPs (but slower than deep learning models). We test the performance of the proposed model based on both synthesized and real-world datasets. For comparison, several widely used machine learning and deep learning models are trained under the real-world dataset. The results demonstrate that the BCEGP model performs well in predictive accuracy, computational speed, and applicability, which can be a promising method for transportation problems.