TY - GEN
T1 - A Mixture Model-based Clustering Method for Fundamental Diagram Calibration Applied in Large Network Simulation
AU - Wang, Ding
AU - Ozbay, Kaan
AU - Bian, Zilin
N1 - Funding Information:
ACKNOWLEDGMENT The work is supported by the C2SMART University Transportation Center. The contents of this paper present views of the authors who are responsible for the facts and accuracy of the data presented herein. The contents of the paper do not reflect the official views or policies of the agencies.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/9/20
Y1 - 2020/9/20
N2 - In traditional methods, fundamental diagrams (FDs) were calibrated offline with a limited number of links. Although few recent studies have paid attention to employing cluster techniques to calibrate link FDs for network level analysis, they were mainly focused on heuristic clustering methods, such as k-means and hierarchical clustering algorithm which might lead to poor performance when there are overlaps between clusters. This paper proposed a mixture model-based clustering framework to calibrate link FDs for network level simulation. This method can be applied to discover a relatively small number of representative link FDs when simulating very large networks with time and budget constraints. In addition, the proposed method can be used to investigate the spatial distribution of links with similar FDs. The proposed method is tested with 567 links using one year's data from the Northern California.
AB - In traditional methods, fundamental diagrams (FDs) were calibrated offline with a limited number of links. Although few recent studies have paid attention to employing cluster techniques to calibrate link FDs for network level analysis, they were mainly focused on heuristic clustering methods, such as k-means and hierarchical clustering algorithm which might lead to poor performance when there are overlaps between clusters. This paper proposed a mixture model-based clustering framework to calibrate link FDs for network level simulation. This method can be applied to discover a relatively small number of representative link FDs when simulating very large networks with time and budget constraints. In addition, the proposed method can be used to investigate the spatial distribution of links with similar FDs. The proposed method is tested with 567 links using one year's data from the Northern California.
UR - http://www.scopus.com/inward/record.url?scp=85099651940&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85099651940&partnerID=8YFLogxK
U2 - 10.1109/ITSC45102.2020.9294346
DO - 10.1109/ITSC45102.2020.9294346
M3 - Conference contribution
AN - SCOPUS:85099651940
T3 - 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020
BT - 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 23rd IEEE International Conference on Intelligent Transportation Systems, ITSC 2020
Y2 - 20 September 2020 through 23 September 2020
ER -