A Bayesian Learning Network for Traffic Speed Forecasting with Uncertainty Quantification

Wu，Ying; Yu，James J.Q.

10.1109/IJCNN52387.2021.9533457

2021-07-18

International Joint Conference on Neural Networks (IJCNN)

2161-4393

978-1-6654-4597-9

Proceedings of the International Joint Conference on Neural Networks

2021-July

1-7

JUL 18-22, 2021

null,null,ELECTR NETWORK

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

Intelligent transportation systems (ITS) depend on accurate and reliable traffic speed prediction to improve the safety, efficiency, and sustainability of transportation activities. Recently, deep learning approaches have significantly contributed to the development of ITS, but are still facing challenges in cyber-physical context due to the aleatoric uncertainty of increasingly uncertain traffic data and epistemic uncertainty of point-to-point estimation training models. In this work, a Bayesian deep learning model reframing with a universal traffic forecasting framework is devised for traffic speed forecasting with uncertainty quantification. The key idea of proposed network is to introduce time-series features in a latent distribution space. Compared to traditional point estimation neural networks, case studies show that the proposed model can predict more reliable results in cross domain learning tests and is capable of discovering good feature representations in missing traffic data or data-deficient scenarios.

Deep learning Uncertainty Neural networks Decision making Estimation Predictive models Data models

第一 ; 通讯

英语

CPCI-S ; EI

General Program of Guangdong Basic and Applied Basic Research Foundation[2019A1515011032] ; Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation[2020B121201001]

Computer Science ; Engineering

Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Engineering, Electrical & Electronic

WOS:000722581701047

20214110996569

Bayesian networks ; Deep learning ; Forecasting ; Intelligent systems ; Intelligent vehicle highway systems ; Speed

Highway Systems:406.1 ; Ergonomics and Human Factors Engineering:461.4 ; Artificial Intelligence:723.4 ; Computer Applications:723.5 ; Combinatorial Mathematics, Includes Graph Theory, Set Theory:921.4 ; Probability Theory:922.1

2-s2.0-85116455184

Scopus

Southern University of Science and Technology,Department of Computer Science and Engineering,Shenzhen,518055,China

Wu，Ying,Yu，James J.Q.. A Bayesian Learning Network for Traffic Speed Forecasting with Uncertainty Quantification[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2021:1-7.