Managing Spatial Graph Dependencies in Large Volumes of Traffic Data for Travel-Time Prediction-Reference-Cited by-同舟云学术

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Managing Spatial Graph Dependencies in Large Volumes of Traffic Data for Travel-Time Prediction

Published:2016-06 Issue:6 Volume:17 Page:1678-1687
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Salamanis Athanasios,Kehagias Dionysios D.,Filelis-Papadopoulos Christos K.,Tzovaras Dimitrios,Gravvanis George A.

Funder

European Commission

FP7 Collaborative Project MOVESMART

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/6979/7479588/07310861.pdf?arnumber=7310861

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2. A local global attention based spatiotemporal network for traffic flow forecasting;Cluster Computing;2024-04-11

3. Exploring the potential of market-available connected vehicle data in border crossing time estimation;Transactions in Urban Data, Science, and Technology;2024-01-31

4. AGNP: Network-wide short-term probabilistic traffic speed prediction and imputation;Communications in Transportation Research;2023-12

5. Spatial matrices for short-term traffic forecasting based on time series;Latin American Transport Studies;2023-12

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