Sökning: id:"swepub:oai:DiVA.org:kth-256047" >
Link speed predicti...
Link speed prediction for signalized urban traffic network using a hybrid deep learning approach
-
- Zhang, Tong (författare)
- State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
-
- Jin, Junchen (författare)
- KTH,Byggvetenskap,Smart Transportation Research Institute, Enjoyor Co. Ltd, Hangzhou, 310030, China and Engineering Research Center of Intelligent Transport of Zhejiang Province, Hangzhou 310030, China.
-
- Yang, Hui (författare)
- State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
-
visa fler...
-
- Guo, Haifeng (författare)
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310013, China ; Enjoyor Co., Ltd, Hangzhou 310030, China.
-
- Ma, Xiaoliang (författare)
- KTH,Byggvetenskap
-
visa färre...
-
(creator_code:org_t)
- 2019
- 2019
- Engelska.
- Relaterad länk:
-
https://www.itsc2019...
-
visa fler...
-
https://ieeexplore.i...
-
https://kth.diva-por... (primary) (Raw object)
-
https://urn.kb.se/re...
-
https://doi.org/10.1...
-
visa färre...
Abstract
Ämnesord
Stäng
- Predicting traffic speed is of importance in transportation management. Signalized road networks manifest highly dynamic speed patterns that are challenging to model and predict. We propose a hybrid deep-learning-based approach for link speed prediction, aiming at capturing heterogeneous spatiotemporal correlations between road intersections. After transforming original road networks and intersections into graphs, this approach leverages a layered graph convolution network structure to model traffic speed variations at both intersection and road network levels. The two levels are combined through a fully connected neural layer. Neural spatiotemporal attention mechanisms are applied to modulate the most relevant periodical traffic information during signal cycles. The proposed approach was evaluated using real-world speed data collected in Hangzhou City, China. Experiments demonstrate that the proposed approach can offer a scalable and effective solution for predicting short-term speed for signalized road networks.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik -- Transportteknik och logistik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Civil Engineering -- Transport Systems and Logistics (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- Transportsystem
- Transport Systems
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)