  This study identifies the limitations and underlying characteristics of urban
mobility networks that influence the performance of the gravity model. The
gravity model is a widely-used approach for estimating and predicting
population flows in urban mobility networks, assuming the scale-free property.
Prior studies have reported good performance results for the gravity model at
certain levels of aggregation. However, the characteristics of urban mobility
networks might vary depending on the spatial and temporal resolutions of data.
Hence, the sensitivity of gravity model performance to variation in the level
of aggregation of data and the temporal and spatial scale of urban mobility
networks needs to be examined. The basic gravity model is tested on urban
mobility networks on an hourly and daily scale using fine-grained
location-based human mobility data for multiple US metropolitan counties to
address this gap. The findings suggest that: (1) finer-scale urban mobility
networks do not demonstrate a scale-free property; (2) the performance of the
basic gravity model decays for predicting population flow in the finer-scale
urban mobility networks; (3) the variations in population density distribution
and mobility network structure and properties across counties do not
significantly influence the performance of gravity models. Hence, gravity
models may not be suitable for modeling urban mobility networks with daily or
hourly aggregation of census tract to census tract movements. The findings
highlight the need for new urban mobility network models or machine learning
approaches to predict fine-scale and high temporal-resolution urban mobility
networks better.
