Hybrid Deep Spatio-Temporal Models for Traffic Flow Prediction on Holidays and Under Adverse Weather

Hybrid Deep Spatio-Temporal Models for Traffic Flow Prediction on Holidays and Under Adverse Weather

Three hybrid deep spatio-temporal models are proposed to accurately predict traffic flow under normal conditions, on holidays and under adverse weather. Each of the proposed models consists of the global and target parts, and fuses the weather and traffic flow data obtained from the target and upstr...

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Autores principales: Wensong Zhang, Ronghan Yao, Xiaojing Du, Jinsong Ye
Formato: article
Lenguaje:EN
Publicado: IEEE 2021
Materias:
Traffic flow prediction
holidays
adverse weather
hybrid deep spatio-temporal model
convolutional long short-term memory (ConvLSTM)
Electrical engineering. Electronics. Nuclear engineering
TK1-9971
Acceso en línea:https://doaj.org/article/de72da522d1c45ee95aba4884296acc4
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https://doaj.org/article/de72da522d1c45ee95aba4884296acc4

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