Traffic Flow Online Prediction Based on a Generative Adversarial Network with Multi-Source Data

Traffic Flow Online Prediction Based on a Generative Adversarial Network with Multi-Source Data

Traffic prediction is essential for advanced traffic planning, design, management, and network sustainability. Current prediction methods are mostly offline, which fail to capture the real-time variation of traffic flows. This paper establishes a sustainable online generative adversarial network (GA...

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Bibliographic Details
Main Authors: Tuo Sun, Bo Sun, Zehao Jiang, Ruochen Hao, Jiemin Xie
Format: article
Language:EN
Published: MDPI AG 2021
Subjects:
traffic flow prediction
long short-term memory
convolutional neural network
improved generating adversarial network
rolling time domain
multi-dimensional indicators
Environmental effects of industries and plants
TD194-195
Renewable energy sources
TJ807-830
Environmental sciences
GE1-350
Online Access:https://doaj.org/article/e47cb2e205de4e02832f6869a1831cc9
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https://doaj.org/article/e47cb2e205de4e02832f6869a1831cc9

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