Enabling Factors and Durations Data Analytics for Dynamic Freight Parking Limits

Freight parking operations occur amid conflicting conditions of public space scarcity, competition with other users, and the inefficient management loading zones (LZ) at cities’ curbside. The dynamic nature freight operations, static LZ provision regulation, accentuate these specific peak times. This generates supply–demand mismatches infrastructure. These have motivated development Smart that bring together technology, infrastructure, data analytics to allocate define duration limits based on users’ needs. Although unlock possibility a responsive management, there is narrow understanding factors analytical tools support their definition. Therefore, aim this paper twofold. Firstly, identify for enabling durations policies. Secondly, assess estimate occupation levels operational locational features. Semi-structured interviews focus group analyses showed use assessment, demand estimation, enforcement capabilities, sharing strategies are most relevant when defining limits. used quantitative models different study using tracked from City Vic (Spain). CatBoost outperformed machine learning (ML) algorithms queuing in estimating durations. contributes field by how definition, curbside management.