The TimeGeo modeling framework for urban mobility without travel surveys

Significance Individual mobility models are important in a wide range of application areas. Current mainstream urban mobility models require sociodemographic information from costly manual surveys, which are in small sample sizes and updated in low frequency. In this study, we propose an individual mobility modeling framework, TimeGeo, that extracts required features from ubiquitous, passive, and sparse digital traces in the information and communication technology era. The model is able to generate individual trajectories in high spatial–temporal resolutions, with interpretable mechanisms and parameters capturing heterogeneous individual travel choices. The modeling framework can flexibly adapt to input data with different resolutions, and be further extended for various modeling purposes. Well-established fine-scale urban mobility models today depend on detailed but cumbersome and expensive travel surveys for their calibration. Not much is known, however, about the set of mechanisms needed to generate complete mobility profiles if only using passive datasets with mostly sparse traces of individuals. In this study, we present a mechanistic modeling framework (TimeGeo) that effectively generates urban mobility patterns with resolution of 10 min and hundreds of meters. It ties together the inference of home and work activity locations from data, with the modeling of flexible activities (e.g., other) in space and time. The temporal choices are captured by only three features: the weekly home-based tour number, the dwell rate, and the burst rate. These combined generate for each individual: (i) stay duration of activities, (ii) number of visited locations per day, and (iii) daily mobility networks. These parameters capture how an individual deviates from the circadian rhythm of the population, and generate the wide spectrum of empirically observed mobility behaviors. The spatial choices of visited locations are modeled by a rank-based exploration and preferential return (r-EPR) mechanism that incorporates space in the EPR model. Finally, we show that a hierarchical multiplicative cascade method can measure the interaction between land use and generation of trips. In this way, urban structure is directly related to the observed distance of travels. This framework allows us to fully embrace the massive amount of individual data generated by information and communication technologies (ICTs) worldwide to comprehensively model urban mobility without travel surveys.