Strategic zoning approach for urban areas: towards a shared transportation system

Investigating downstream freight demand is a prerequisite to accomplishing the overall strategic implementation of transportation systems. Machine learning has recently become widely applied in order to support decision-making in several logistic operational levels: travel/arrival time prediction, occupancy forecasting of logistic spaces, route optimization and so on. Nevertheless, strategic decision-making often overlooks flow tendencies forecasting. Targeting this perspective, the present paper aims at proposing an urban zoning approach based on time series forecasting of supply chain demand through clustering customers. To conduct our approach, we have selected a set of machine learning algorithms that are believed to be robust according to the literature and the achieved accuracy benchmarks. Considering real-life data-based computational results, a number of analytical insights are illustrated.