Supervised Weighting-Online Learning Algorithm for Short-Term Traffic Flow Prediction

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 predic...

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Vydáno v:IEEE transactions on intelligent transportation systems Ročník 14; číslo 4; s. 1700 - 1707
Hlavní autoři: Young-Seon Jeong, Young-Ji Byon, Mendonca Castro-Neto, Manoel, Easa, Said M.
Médium: Journal Article
Jazyk:angličtina
Vydáno: IEEE 01.12.2013
Témata:
Artificial neural networks
Data models
Intelligent transportation systems (ITSs)
online learning weighted support-vector regression (OLWSVR)
Prediction algorithms
Predictive models
short-term traffic flow forecast
supervised algorithm
Support vector machines
Traffic control
ISSN:1524-9050, 1558-0016
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Shrnutí: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.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2013.2267735