Modeling aggregate human mobility patterns in cities based on the spatial distribution of local infrastructure

Understanding human mobility patterns in urban areas is key to solving a wide range of socio-technical problems at the human-infrastructure interface. Extending intervening opportunities concept, we showcase data-driven, network-based model that reproduces aggregate cities. Using this model, create digital replication daily travel across different trip purposes 5 U.S. metropolitan and compare results against publicly available reference data. We find our proposed explains large fraction variation mean median distance In particular, it accurately captures effect density on patterns. These findings add evidence are strongly governed by structure built environment. discuss implications for ongoing transformation cities developing more sophisticated models replicate behavior based crowd-sourced, spatio-temporal data streams.