Machine learning-based ranking of factors influencing human movement purposes for supporting human-infrastructure interaction modeling

Modeling, predicting, and controlling the interactions between humans and civil infrastructure systems can simultaneously improve the operational efficiency of infrastructure systems and the satisfaction of infrastructure users. The first step toward achieving this goal is to model human-To-infrastructure interaction, which in most cases is driven by human movements (e.g., moving from an origin location to a destination requires using the transportation infrastructure connecting the two). To this end, this paper aims to conduct a machine learning-based data-driven analysis to rank the importance of factors influencing human movement purposes, thereby identifying highly influential factors to support subsequent human-To-infrastructure interaction modeling. The research methodology included: (1) representing movement instances using spatial and land use, temporal, and demographic features; and (2) conducting feature ranking per movement purpose type using the logistic regression algorithm. As a preliminary work, this paper focuses on presenting the research methodology, and analyzing and discussing the feature ranking results.