Sökning: WFRF:(Ma Xiaoliang Docent) >
A GAN-Based Short-T...
A GAN-Based Short-Term Link Traffic Prediction Approach for Urban Road Networks Under a Parallel Learning Framework
-
Jin, J. (författare)
-
Rong, D. (författare)
-
Zhang, T. (författare)
-
visa fler...
-
Ji, Q. (författare)
-
Guo, H. (författare)
-
Lv, Y. (författare)
-
- Ma, Xiaoliang, Docent (författare)
- KTH,Transportplanering
-
Wang, F. -Y (författare)
-
visa färre...
-
(creator_code:org_t)
- Institute of Electrical and Electronics Engineers (IEEE), 2022
- 2022
- Engelska.
-
Ingår i: IEEE transactions on intelligent transportation systems (Print). - : Institute of Electrical and Electronics Engineers (IEEE). - 1524-9050 .- 1558-0016. ; 23:9, s. 16185-16196
- Relaterad länk:
-
https://urn.kb.se/re...
-
visa fler...
-
https://doi.org/10.1...
-
visa färre...
Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik -- Transportteknik och logistik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Civil Engineering -- Transport Systems and Logistics (hsv//eng)
Nyckelord
- 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
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
Hitta via bibliotek
Till lärosätets databas