Sustainable Urban Freight Optimization for Isobenefit Cities: Integrating Neural Networks and Graph Theory

Urban logistics serves as a cornerstone for efficient freight transport and sustainable city development, particularly in contexts challenged by congestion and environmental pressures. This research examines the restructuring of the urban logistics system in Sfax, Tunisia—an expanding industrial and economic center increasingly burdened by traffic congestion. Through a comprehensive analytical framework, the study identifies the primary determinants influencing freight transport operations and develops a phased policy roadmap to enhance logistical efficiency. Neural Network Modeling is employed to evaluate the effects of multiple transport-related variables on logistics performance, while Graph Theory is utilized to represent spatial and functional interconnections, facilitating the visualization of freight flows and supporting evidence-based decision-making. The results emphasize the crucial role of managing truck circulation within Sfax’s urban core. Accordingly, a three-phase reorganization plan is proposed to optimize freight mobility, alleviate congestion, and advance sustainable urban growth. The methodological approach and policy insights offer practical guidance applicable to other metropolitan areas facing similar logistical challenges.