Identifying clusters to implement urban logistics best practices: the case of São Paulo
Cities have different characteristics, and policy measures applied in different urban areas can only result in different associated impacts. The purpose of this paper is to apply a data-driven methodology to identify clusters to guide São Paulo’s urban logistics policy and practice decisions. The methodology uses relevant variables for urban logistics – establishments’ concentration, population, infrastructure (road capacity and road density), and regulation data – in order to perform two statistical analysis: Principal Component Analysis and K-means clustering. The results suggest segmenting the city into five different clusters, as a basis for further cluster-specific analyses and implementation of practices.