Short‐term traffic flow prediction with linear conditional Gaussian Bayesian network

TLDR

Traffic flow prediction is essential for ITS, yet prior work largely treated it as a time series, ignoring upstream spatial relationships and correlations with speed and density. The study employs a linear conditional Gaussian Bayesian network to incorporate spatial, temporal, and speed information for short‑term traffic flow prediction. The authors use a microscopic traffic simulation dataset to evaluate a linear conditional Gaussian Bayesian network that handles both continuous and discrete variables, comparing its performance to other popular approaches and assessing the added value of spatial and speed data. Including both spatial and speed data significantly improves prediction accuracy. © 2016 John Wiley & Sons, Ltd.

Abstract

Summary Traffic flow prediction is an essential part of intelligent transportation systems (ITS). Most of the previous traffic flow prediction work treated traffic flow as a time series process only, ignoring the spatial relationship from the upstream flows or the correlation with other traffic attributes like speed and density. In this paper, we utilize a linear conditional Gaussian (LCG) Bayesian network (BN) model to consider both spatial and temporal dimensions of traffic as well as speed information for short‐term traffic flow prediction. The LCG BN allows both continuous and discrete variables, which enables the consideration of categorical variables in traffic flow prediction. A microscopic traffic simulation dataset is used to test the performance of the proposed model compared to other popular approaches under different predicting time intervals. In addition, the authors investigate the importance of spatial data and speed data in flow prediction by comparing models with different levels of information. The results indicate that the prediction accuracy will increase significantly when both spatial data and speed data are included. Copyright © 2016 John Wiley & Sons, Ltd.