Leveraging Probe Data and Machine Learning to Derive and Interpret Macroscopic Fundamental Diagrams Across U.S. Cities-Reference-Cited by-同舟云学术

Leveraging Probe Data and Machine Learning to Derive and Interpret Macroscopic Fundamental Diagrams Across U.S. Cities

Published:2023-12-15 Issue: Volume:45 Page:2606-2615
ISSN:
Container-title:2023 IEEE International Conference on Big Data (BigData)
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Author:

Jin Ling¹,Xu Xiaodan¹,Wang Yuhan¹,Sadabadi Kaveh Farokhi²,Lazar Alina³,Don Duleep Rathgamage⁴,Needell Zachary⁵,Spurlock C Anna⁵,Amirgholy Mahyar⁶,Asudegi Mona⁷

Affiliation:

1. Energy Technology Area, Berkeley National Laboratory,Berkeley,CA,94720

2. University of Maryland,Center for Advanced Transportation Technology,College Park,MD,20742

3. Youngstown State University,Department of Computer Science and Information,Youngstown,OH,44555

4. Kennesaw State University,School of Data Science and Analytics,Marietta,GA,30060

5. Energy Analysis and Environmental Impacts Division Lawrence Berkeley National Laboratory,Berkeley,CA,94720

6. Kennesaw State University,Civil and Environmental Engineering Department,Marietta,GA,30060

7. Office of Transportation Policy Studies Federal Highway Administration,Washington DC,D.C.,20590

Funder

Federal Highway Administration

U.S. Department of Energy

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10385234/10386078/10386591.pdf?arnumber=10386591

Reference34 articles.

1. Existence of urban-scale macroscopic fundamental diagrams: Some experimental findings

2. An analytical approximation for the macroscopic fundamental diagram of urban traffic

3. Urban gridlock: Macroscopic modeling and mitigation approaches

4. Macroscopic relations of urban traffic variables: Bifurcations, multivaluedness and instability

5. Exploratory Analysis of Signal Coordination Impacts on Macroscopic Fundamental Diagram