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 this paper is to identify mobility strategy profiles considering four key dimensions: time, space, money, and social interaction. Data from a seven-day activity, travel, expenditure, and social interaction diary applied to a sample of residents from the city of Concepción, Chile, is used. A two-step approach based on machine learning techniques is adopted. First, we use a Self-Organizing Map algorithm to identify seven distinct mobility strategies, each characterized by distinctive behaviors. These profiles are identified through 18 weekday and weekend daily behavior variables measuring the four key dimensions mentioned above, with an explicit focus on transport mode use. In the second step, we use a Decision Tree algorithm to profile the mobility strategies by means of personal and household sociodemographic variables. The results show interesting links among the dimensions of analysis within these profiles, such as connections between monetary expenditure on leisure and daily social interaction, and that profiles with higher private vehicle modal split tend to present higher levels of social interaction with people in their social network than public transit users.