TY - JOUR
T1 - An efficient multi-modal urban transportation network partitioning approach for three-dimensional macroscopic fundamental diagram
AU - Tang, Siyi
AU - Zheng, Fangfang
AU - Zheng, Nan
AU - Liu, Xiaobo
N1 - Funding Information:
This work is supported by National Key R&D Program of China under grant 2021YFB1600100 . We would also like to express our sincere gratitude to Dr. Allister Loder for providing the data required for this study.
Publisher Copyright:
© 2024 Elsevier B.V.
PY - 2024/3/1
Y1 - 2024/3/1
N2 - The three-dimensional macroscopic fundamental diagram (3D-MFD) provides a comprehensive understanding of the relationship between network efficiency and accumulation of cars and buses in transportation networks. In this paper, we propose a novel approach to partition multi-modal urban transportation networks into several homogeneous sub-regions to obtain well-shaped 3D-MFDs for each sub-region. The proposed approach consists of three stages: an initial partitioning process using the Symmetric Non-negative Matrix Factorization (SNMF) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), iterative merging of the initial partitioned sub-regions based on SNMF and TOPSIS, and optimization of the sub-regional boundaries, considering the traffic network's link hierarchy, to accurately capture its physical characteristics. Comparative experiments utilizing real data from the Zurich traffic network demonstrate that our proposed method achieves remarkable partitioning results, as supported by evaluation metrics. Furthermore, the partitioning results obtained through our proposed approach exhibit more favorable network physical characteristics, providing a solid foundation for implementing control strategies to enhance network efficiency.
AB - The three-dimensional macroscopic fundamental diagram (3D-MFD) provides a comprehensive understanding of the relationship between network efficiency and accumulation of cars and buses in transportation networks. In this paper, we propose a novel approach to partition multi-modal urban transportation networks into several homogeneous sub-regions to obtain well-shaped 3D-MFDs for each sub-region. The proposed approach consists of three stages: an initial partitioning process using the Symmetric Non-negative Matrix Factorization (SNMF) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), iterative merging of the initial partitioned sub-regions based on SNMF and TOPSIS, and optimization of the sub-regional boundaries, considering the traffic network's link hierarchy, to accurately capture its physical characteristics. Comparative experiments utilizing real data from the Zurich traffic network demonstrate that our proposed method achieves remarkable partitioning results, as supported by evaluation metrics. Furthermore, the partitioning results obtained through our proposed approach exhibit more favorable network physical characteristics, providing a solid foundation for implementing control strategies to enhance network efficiency.
KW - 3D-MFD
KW - Multi-modal
KW - Network partitioning
KW - Physical characteristics
KW - Real data
KW - Urban transportation network
UR - http://www.scopus.com/inward/record.url?scp=85184150951&partnerID=8YFLogxK
U2 - 10.1016/j.physa.2023.129487
DO - 10.1016/j.physa.2023.129487
M3 - Article
AN - SCOPUS:85184150951
SN - 0378-4371
VL - 637
JO - Physica A: Statistical Mechanics and its Applications
JF - Physica A: Statistical Mechanics and its Applications
M1 - 129487
ER -