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

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...

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Bibliographic Details
Main Authors: Zongtao Duan, Kai Zhang, Zhe Chen, Zhiyuan Liu, Lei Tang, Yun Yang, Yuanyuan Ni
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Prediction of dynamic origin-destination flows
hybrid deep neural network
demands for taxis
GPS trajectory of taxis
Online Access:https://ieeexplore.ieee.org/document/8826268/

Internet

https://ieeexplore.ieee.org/document/8826268/

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