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A GAN-Based Short-T...
A GAN-Based Short-Term Link Traffic Prediction Approach for Urban Road Networks Under a Parallel Learning Framework
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Jin, J. (author)
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Rong, D. (author)
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Zhang, T. (author)
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Ji, Q. (author)
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Guo, H. (author)
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Lv, Y. (author)
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- Ma, Xiaoliang, Docent (author)
- KTH,Transportplanering
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Wang, F. -Y (author)
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(creator_code:org_t)
- Institute of Electrical and Electronics Engineers (IEEE), 2022
- 2022
- English.
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In: IEEE transactions on intelligent transportation systems (Print). - : Institute of Electrical and Electronics Engineers (IEEE). - 1524-9050 .- 1558-0016. ; 23:9, s. 16185-16196
- Related links:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
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- Road link speed is often employed as an essential measure of traffic state in the operation of an urban traffic network. Not only real-time traffic demand but also signal timings and other local planning factors are major influential factors. This paper proposes a short-term traffic speed prediction approach, called PL-WGAN, for urban road networks, which is considered an important part of a novel parallel learning framework for traffic control and operation. The proposed method applies Wasserstein Generative Adversarial Nets (WGAN) for robust data-driven traffic modeling using a combination of generative neural network and discriminative neural network. The generative neural network models the road link features of the adjacent intersections and the control parameters of intersections using a hybrid graph block. In addition, the spatial-temporal relations are captured by stacking a graph convolutional network (GCN), a recurrent neural network (RNN), and an attention mechanism. A comprehensive computational experiment was carried out including comparing model prediction and computational performances with several state-of-the-art deep learning models. The proposed approach has been implemented and applied for predicting short-term link traffic speed in a large-scale urban road network in Hangzhou, China. The results suggest that it provides a scalable and effective traffic prediction solution for urban road networks.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik -- Transportteknik och logistik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Civil Engineering -- Transport Systems and Logistics (hsv//eng)
Keyword
- Short-term link speed prediction
- signalized urban networks
- Wasserstein generative adversarial network
- Computer architecture
- Deep neural networks
- Forecasting
- Generative adversarial networks
- Roads and streets
- Speed
- Street traffic control
- Deep learning
- Generator
- Link speed
- Predictive models
- Road
- Signalized urban network
- Speed prediction
- Urban networks
- Wasserstein generative adversarial network.
- Recurrent neural networks
Publication and Content Type
- ref (subject category)
- art (subject category)
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