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Sökning: onr:"swepub:oai:DiVA.org:kth-320811" > A GAN-Based Short-T...

LIBRIS Formathandbok  (Information om MARC21)
FältnamnIndikatorerMetadata
00003825naa a2200613 4500
001oai:DiVA.org:kth-320811
003SwePub
008221107s2022 | |||||||||||000 ||eng|
0247 a https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3208112 URI
0247 a https://doi.org/10.1109/TITS.2022.31483582 DOI
040 a (SwePub)kth
041 a engb eng
042 9 SwePub
072 7a ref2 swepub-contenttype
072 7a art2 swepub-publicationtype
1001 a Jin, J.4 aut
2451 0a A GAN-Based Short-Term Link Traffic Prediction Approach for Urban Road Networks Under a Parallel Learning Framework
264 1b Institute of Electrical and Electronics Engineers (IEEE),c 2022
338 a print2 rdacarrier
500 a QC 20221107
520 a 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. 
650 7a TEKNIK OCH TEKNOLOGIERx Samhällsbyggnadsteknikx Transportteknik och logistik0 (SwePub)201052 hsv//swe
650 7a ENGINEERING AND TECHNOLOGYx Civil Engineeringx Transport Systems and Logistics0 (SwePub)201052 hsv//eng
653 a Short-term link speed prediction
653 a signalized urban networks
653 a Wasserstein generative adversarial network
653 a Computer architecture
653 a Deep neural networks
653 a Forecasting
653 a Generative adversarial networks
653 a Roads and streets
653 a Speed
653 a Street traffic control
653 a Deep learning
653 a Generator
653 a Link speed
653 a Predictive models
653 a Road
653 a Signalized urban network
653 a Speed prediction
653 a Urban networks
653 a Wasserstein generative adversarial network.
653 a Recurrent neural networks
7001 a Rong, D.4 aut
7001 a Zhang, T.4 aut
7001 a Ji, Q.4 aut
7001 a Guo, H.4 aut
7001 a Lv, Y.4 aut
7001 a Ma, Xiaoliang,c Docentu KTH,Transportplanering4 aut0 (Swepub:kth)u1i7xzbr
7001 a Wang, F. -Y4 aut
7102 a KTHb Transportplanering4 org
7730 t IEEE transactions on intelligent transportation systems (Print)d : Institute of Electrical and Electronics Engineers (IEEE)g 23:9, s. 16185-16196q 23:9<16185-16196x 1524-9050x 1558-0016
8564 8u https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-320811
8564 8u https://doi.org/10.1109/TITS.2022.3148358

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