Shopping Trip Choice Prediction for Assessing Store Relocation: a Joint Data-Driven and Behavioural Modelling Approach

Abstract This paper proposes and tests a methodology to analyse end consumers’ choices in terms of shopping destination for a store selling culture products, comparing a city centre location to a peripheral one. The proposed methodology begins with a stated preferences survey and incorporates a conditional tree classification algorithm to pre-select the predictors (attributes), then used to develop a discrete choice model. To validate the methodology, a real-world case study was carried out, including a survey with over one thousand customer responses. The findings reveal noteworthy insights into customer attitudes toward relocation, distinguishing frequent from non-frequent users and examining factors such as travel distance and visit frequency. These results offer valuable guidance for retailers and policy makers in shaping city logistics scenarios, highlighting the potential transformations in urban freight flows driven by changes in retail land use.