Data-Driven Distance Metrics for Kriging-Short-Term Urban Traffic State Prediction-Reference-Cited by-同舟云学术

Data-Driven Distance Metrics for Kriging-Short-Term Urban Traffic State Prediction

Published:2023-06 Issue:6 Volume:24 Page:6268-6279
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Varga Balázs¹^ORCID,Pereira Mike²^ORCID,Kulcsár Balázs³^ORCID,Pariota Luigi⁴^ORCID,Péni Tamás⁵^ORCID

Affiliation:

1. Department of Control for Transportation and Vehicle Systems, Faculty of Transportation Engineering and Vehicle Engineering, Budapest University of Technology and Economics, Budapest, Hungary

2. Department of Mathematical Sciences, Chalmers University of Technology, Gothenburg, Sweden

3. Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden

4. Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, Naples, Italy

5. Systems and Control Laboratory, Institute for Computer Science and Control (SZTAKI), Budapest, Hungary

Funder

European Union within the framework of the National Laboratory for Autonomous Systems

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/6979/10139322/10070380.pdf?arnumber=10070380

Reference46 articles.

1. PrePCT: Traffic congestion prediction in smart cities with relative position congestion tensor

2. Flexibility and efficiency enhancements for constrained global design optimization with Kriging approximations;sasena,2002

3. A spatial-temporal approach for traffic status analysis and prediction based on Bi-LSTM structure

4. Missing data imputation for traffic flow speed using spatio-temporal cokriging

5. Deep Multi-Scale Convolutional LSTM Network for Travel Demand and Origin-Destination Predictions

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