Sökning: WFRF:(Ma Xiaoliang Docent) > A GAN-Based Short-T...
Fältnamn | Indikatorer | Metadata |
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000 | 03825naa a2200613 4500 | |
001 | oai:DiVA.org:kth-320811 | |
003 | SwePub | |
008 | 221107s2022 | |||||||||||000 ||eng| | |
024 | 7 | a https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3208112 URI |
024 | 7 | a https://doi.org/10.1109/TITS.2022.31483582 DOI |
040 | a (SwePub)kth | |
041 | a engb eng | |
042 | 9 SwePub | |
072 | 7 | a ref2 swepub-contenttype |
072 | 7 | a art2 swepub-publicationtype |
100 | 1 | a Jin, J.4 aut |
245 | 1 0 | a A GAN-Based Short-Term Link Traffic Prediction Approach for Urban Road Networks Under a Parallel Learning Framework |
264 | 1 | b 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 | 7 | a TEKNIK OCH TEKNOLOGIERx Samhällsbyggnadsteknikx Transportteknik och logistik0 (SwePub)201052 hsv//swe |
650 | 7 | a 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 | |
700 | 1 | a Rong, D.4 aut |
700 | 1 | a Zhang, T.4 aut |
700 | 1 | a Ji, Q.4 aut |
700 | 1 | a Guo, H.4 aut |
700 | 1 | a Lv, Y.4 aut |
700 | 1 | a Ma, Xiaoliang,c Docentu KTH,Transportplanering4 aut0 (Swepub:kth)u1i7xzbr |
700 | 1 | a Wang, F. -Y4 aut |
710 | 2 | a KTHb Transportplanering4 org |
773 | 0 | 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 |
856 | 4 8 | u https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-320811 |
856 | 4 8 | u https://doi.org/10.1109/TITS.2022.3148358 |
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