Learning Spatio-Temporal Heterogeneity of Urban Truck Dwelling Behavior With a Contrastive Graph Representation Framework

Urban freight transport is becoming increasingly complex under rapid urbanization. Understanding spatio-temporal distribution of truck dwelling behavior is essential to improve logistics efficiency and urban sustainability. Yet, the freight activity patterns are not well captured by static representation methods. This study introduces a contrastive graph representation learning framework to characterize the spatio-temporal and behavioral features of truck dwelling locations using large-scale GPS trajectory data. We construct a time-varying directed truck flow network. Building on edge convolution, we develop a modified Edge Convolution Network (ECN) with attention-based aggregation to learn spatial dependencies and temporal dynamics. These embeddings are used to delineate logistics activity zones through unsupervised clustering. The framework is tested and evaluated with a massive truck trajectory dataset and compared with other baseline methods. The results prove that the modified ECN model achieves better performance than baselines and reveals interpretable spatio-temporal activity patterns providing valuable insights in logistics amelioration.