Aligning urban freight efficiency and sustainability: Integrating neural network modelling and PESTEL analysis

Urban freight systems are essential to economic activity but generate significant environmental, spatial, and social pressures, particularly in rapidly growing cities. This study analyses the restructuring of urban freight transport in Sfax, Tunisia—an industrial and commercial hub facing rising congestion, logistical inefficiencies, and environmental stress. It develops an integrated analytical framework combining Multilayer Perceptron neural network modelling with a PESTEL-based systemic diagnosis to identify the main determinants of freight performance and inform targeted policy design. The study contributes by bridging systemic policy analysis with advanced predictive modelling, offering one of the first comprehensive data-driven assessments of urban freight dynamics in a North African metropolitan context. Results highlight key operational levers—movement scheduling, fleet composition, and carrier organization—as critical drivers of system efficiency and sustainability. Building on these findings, the paper proposes a phased policy roadmap integrating governance reform, spatial reorganization of logistics activities, technological modernization, and strengthened environmental regulation. The framework supports better alignment between transport regulation, land-use planning, and environmental policy, enhancing institutional coordination and urban resilience. Beyond Sfax, the approach provides transferable methodological and policy insights for cities seeking to balance economic performance, environmental sustainability, and urban liveability.