TY - JOUR
T1 - A Fundamental-Diagram-Informed Spatial Partitioning Method for Heterogeneous Traffic Networks
AU - Ding, Fan
AU - Zhao, Yu
AU - Tan, Huachun
AU - Liu, Zhao
AU - Pu, Ziyuan
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2024
Y1 - 2024
N2 - Spatially partitioning heterogeneous traffic networks into multiple sub-networks is crucial for practical tasks such as distributed signal control and model parallel processing. Existing partitioning methods that account for traffic characteristics over a certain period typically require calculating similarities between the time series of all sensors. Due to the quadratic increase in complexity with network size, these methods are inefficient for large-scale networks and extended time periods. Additionally, calculating similarities requires complete data, making such methods highly sensitive to missing data and lacking robustness. To address these issues, this paper proposes a four-step fundamental-diagram-informed traffic network partitioning method. First, spatially adjacent sensors are clustered into sub-clusters. Next, the S3 speed-occupancy function is used to fit the aggregated data of each sub-cluster to extract fundamental diagram information. Then, this information is used to perform secondary clustering to form clusters. Finally, the cluster boundaries are fine-tuned to produce sub-networks with smooth boundaries. The proposed method calculates the parameter similarity between sub-clusters instead of the time series similarity between all sensors. This reduces computational costs and effectively handles data missing. A case study using real-world data verifies the effectiveness of the proposed method and its stability in the presence of missing data. Compared to Spectral Clustering, the total within-cluster variance and NcutSilhouette metric decrease by 5.7% and 17.8%, respectively. The proposed method enhances distributed or parallel tasks on traffic networks by providing stable and meaningful partitioning results. This method is beneficial for the analysis and effective management of complex heterogeneous traffic networks.
AB - Spatially partitioning heterogeneous traffic networks into multiple sub-networks is crucial for practical tasks such as distributed signal control and model parallel processing. Existing partitioning methods that account for traffic characteristics over a certain period typically require calculating similarities between the time series of all sensors. Due to the quadratic increase in complexity with network size, these methods are inefficient for large-scale networks and extended time periods. Additionally, calculating similarities requires complete data, making such methods highly sensitive to missing data and lacking robustness. To address these issues, this paper proposes a four-step fundamental-diagram-informed traffic network partitioning method. First, spatially adjacent sensors are clustered into sub-clusters. Next, the S3 speed-occupancy function is used to fit the aggregated data of each sub-cluster to extract fundamental diagram information. Then, this information is used to perform secondary clustering to form clusters. Finally, the cluster boundaries are fine-tuned to produce sub-networks with smooth boundaries. The proposed method calculates the parameter similarity between sub-clusters instead of the time series similarity between all sensors. This reduces computational costs and effectively handles data missing. A case study using real-world data verifies the effectiveness of the proposed method and its stability in the presence of missing data. Compared to Spectral Clustering, the total within-cluster variance and NcutSilhouette metric decrease by 5.7% and 17.8%, respectively. The proposed method enhances distributed or parallel tasks on traffic networks by providing stable and meaningful partitioning results. This method is beneficial for the analysis and effective management of complex heterogeneous traffic networks.
KW - Fundamental Diagram
KW - K-Medoids Clustering
KW - Spatial Partitioning
KW - Traffic Network
UR - http://www.scopus.com/inward/record.url?scp=85213050776&partnerID=8YFLogxK
U2 - 10.1109/JIOT.2024.3519779
DO - 10.1109/JIOT.2024.3519779
M3 - Article
AN - SCOPUS:85213050776
SN - 2327-4662
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
ER -