Open AccessShort‐term traffic flow prediction with linear conditional Gaussian Bayesian network
Author(s) -
Zhu Zheng,
Peng Bo,
Xiong Chenfeng,
Zhang Lei
Publication year - 2016
Publication title -
journal of advanced transportation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.577
H-Index - 46
eISSN - 2042-3195
pISSN - 0197-6729
DOI - 10.1002/atr.1392
Subject(s) - categorical variable , traffic flow (computer networking) , computer science , traffic generation model , spatial correlation , data mining , intelligent transportation system , gaussian process , bayesian probability , gaussian , bayesian network , term (time) , time series , flow (mathematics) , artificial intelligence , machine learning , engineering , real time computing , mathematics , transport engineering , telecommunications , physics , computer security , quantum mechanics , geometry
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.