A Fundamental-Diagram-Informed Spatial Partitioning Method for Heterogeneous Traffic Networks

Spatially partitioning heterogeneous traffic networks into multiple subnetworks 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 article proposes a four-step fundamental-diagram-informed traffic network partitioning method. First, spatially adjacent sensors are clustered into subclusters. Next, the S3 speed-occupancy function is used to fit the aggregated data of each subcluster 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 subnetworks with smooth boundaries. The proposed method calculates the parameter similarity between subclusters 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.