  Modeling human mobility has a wide range of applications from urban planning
to simulations of disease spread. It is well known that humans spend 80% of
their time indoors but modeling indoor human mobility is challenging due to
three main reasons: (i) the absence of easily acquirable, reliable, low-cost
indoor mobility datasets, (ii) high prediction space in modeling the frequent
indoor mobility, and (iii) multi-scalar periodicity and correlations in
mobility. To deal with all these challenges, we propose WiFiMod, a
Transformer-based, data-driven approach that models indoor human mobility at
multiple spatial scales using WiFi system logs. WiFiMod takes as input
enterprise WiFi system logs to extract human mobility trajectories from
smartphone digital traces. Next, for each extracted trajectory, we identify the
mobility features at multiple spatial scales, macro, and micro, to design a
multi-modal embedding Transformer that predicts user mobility for several hours
to an entire day across multiple spatial granularities. Multi-modal embedding
captures the mobility periodicity and correlations across various scales while
Transformers capture long-term mobility dependencies boosting model prediction
performance. This approach significantly reduces the prediction space by first
predicting macro mobility, then modeling indoor scale mobility, micro-mobility,
conditioned on the estimated macro mobility distribution, thereby using the
topological constraint of the macro-scale. Experimental results show that
WiFiMod achieves a prediction accuracy of at least 10% points higher than the
current state-of-art models. Additionally, we present 3 real-world applications
of WiFiMod - (i) predict high-density hot pockets for policy-making decisions
for COVID19 or ILI, (ii) generate a realistic simulation of indoor mobility,
(iii) design personal assistants.
