A flexible road network partitioning framework for traffic management via graph contrastive learning and multi-objective optimization

Abstract

The partitioning of a heterogeneously loaded road network into homogeneous, compact subregions is a fundamental prerequisite for the implementation of network-level traffic management and control based on the network macroscopic fundamental diagram. This study proposes a flexible road network partitioning framework that leverages the powerful feature extraction capabilities of self-supervised graph neural networks and employs a multi-objective optimization approach to balance regional homogeneity and compactness. A graph contrastive learning model is proposed to extract meaningful node embeddings that incorporate topology and attribute similarity information. Based on the learned node embeddings, the partition is determined by a parameter-free hierarchical clustering method and a subregion identification algorithm. Boundary tuning is then modeled as a bi-objective optimization problem to maximize regional homogeneity and compactness. A Pareto local search algorithm is developed to approximate the Pareto front. This study further demonstrates the extension of the proposed methods to scenarios with missing data. Finally, the methods are validated on real road networks with automatic license plate recognition data.