A Multitask Learning Model for Traffic Flow and Speed Forecasting

A Multitask Learning Model for Traffic Flow and Speed Forecasting

Intelligent Transportation Systems (ITS) research and applications benefit from accurate short-term traffic state forecasting. To improve the forecasting accuracy, this paper proposes a deep learning based multitask learning Gated Recurrent Units (MTL-GRU) with residual mappings. To enhance the perf...

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Bibliographic Details
Main Authors: Kunpeng Zhang, Lan Wu, Zhaoju Zhu, Jiang Deng
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Short-term traffic forecasting
deep learning
multitask learning
feature engineering
Online Access:https://ieeexplore.ieee.org/document/9080108/

Internet

https://ieeexplore.ieee.org/document/9080108/

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