Traffic Prediction With Missing Data: A Multi-Task Learning Approach

Traffic speed prediction based on real-world traffic data is a classical problem in intelligent transportation systems (ITS). Most existing traffic speed prediction models are proposed based on the hypothesis that traffic data are complete or have rare missing values. However, such data collected in...

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Vydáno v:	IEEE transactions on intelligent transportation systems Ročník 24; číslo 4; s. 1 - 14
Hlavní autoři:	Wang, Ao, Ye, Yongchao, Song, Xiaozhuang, Zhang, Shiyao, Yu, James J. Q.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.04.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Coders Data mining Deep learning Encoders-Decoders Feature extraction Intelligent transportation systems Learning Missing data multi-task learning Multitasking Prediction models Predictive models Spatial dependencies spatio-temporal modeling Task analysis Traffic information Traffic models Traffic speed Traffic speed prediction Training
ISSN:	1524-9050, 1558-0016
On-line přístup:	Získat plný text
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