Time, space, money, and social interaction: Using machine learning to classify people’s mobility strategies through four key dimensions

Previous activity-based studies have shown that behavioural outcomes are the result of complex and multidimensional processes. In this context, identifying and characterizing discrete mobility profiles through the classification of people’s behavior is particularly attractive. By facilitating the interpretation of complex, multidimensional processes, such an exercise could help to efficiently target transport policy decisions. The purpose of …