Proximity Features: A Random Forest Approach to the Influence of the Built Environment on Local Travel Behavior

Recent European policies fostering sustainable mobility target urban proximity as a core strategy for a modal shift towards low-carbon modes. Urban proximity, as a characteristic of the built environment, can be studied as a sub-thread of a broad and complex body of literature which associates urban factors such as density or land use mix with observed travel behavior, so as to address their relative influence on the latter. Building on this previous knowledge, the present work addresses the importance of a diverse set of factors on local travel modal choice between walking and other modes, according to the 2018 Household Mobility Survey of the Metropolitan Region of Madrid, and a large variety of demographic and built environment characteristics. The work proposes to address this importance through a workflow on a set of Machine Learning models, filtering different distance thresholds and purposes of the trips, going through a strict feature selection process, and executing under different schema definitions. The resulting models are inspected for accuracy, feature importance, and composition. Results suggest that even small changes in distance thresholds exert a great impact on all models; sociodemographic variables are slightly more important in most models, yet building age, along with other street layout factors, pervasively obtain fairly accurate predictions too.