Prediction of City-Scale Dynamic Taxi Origin-Destination Flows Using a Hybrid Deep Neural Network Combined With Travel Time

Predicting city-scale taxi origin-destination (OD) flows takes an important role in understanding passengers' travel demands as well as managing taxi operation and scheduling. But the complex spatial dependencies and temporal dynamics make this problem challenging. In this paper, a hybrid deep neura...

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Detalles Bibliográficos

Principais autores:	Zongtao Duan, Kai Zhang, Zhe Chen, Zhiyuan Liu, Lei Tang, Yun Yang, Yuanyuan Ni
Formato:	Artigo
Idioma:	Inglês
Publicado:	IEEE 2019-01-01
Series:	IEEE Access
Assuntos:	Prediction of dynamic origin-destination flows hybrid deep neural network demands for taxis GPS trajectory of taxis
Acceso en liña:	https://ieeexplore.ieee.org/document/8826268/
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