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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
Supervised weighting-online learning algorithm for short-term traffic flow prediction
IEEE Transactions on Intelligent Transportation Systems, Volume 14, No. 4, Article 6553284, Year 2013
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Description
Prediction of short-term traffic flow has become one of the major research fields in intelligent transportation systems. Accurately estimated traffic flow forecasts are important for operating effective and proactive traffic management systems in the context of dynamic traffic assignment. For predicting short-term traffic flows, recent traffic information is clearly a more significant indicator of the near-future traffic flow. In other words, the relative significance depending on the time difference between traffic flow data should be considered. Although there have been several research works for short-term traffic flow predictions, they are offline methods. This paper presents a novel prediction model, called online learning weighted support-vector regression (OLWSVR), for short-term traffic flow predictions. The OLWSVR model is compared with several well-known prediction models, including artificial neural network models, locally weighted regression, conventional support-vector regression, and online learning support-vector regression. The results show that the performance of the proposed model is superior to that of existing models. © 2000-2011 IEEE.
Authors & Co-Authors
Jeong, Young Seon
United Arab Emirates, Abu Dhabi
Khalifa University of Science and Technology
Byon, Young Ji
United Arab Emirates, Abu Dhabi
Khalifa University of Science and Technology
Castro-Neto, Manoel Mendonca
Brazil, Fortaleza
Universidade Federal do Ceará
Easa, Said M.
Canada, Toronto
Toronto Metropolitan University
Statistics
Citations: 242
Authors: 4
Affiliations: 3
Identifiers
Doi:
10.1109/TITS.2013.2267735