WiFiMod: Transformer-based Indoor Human Mobility Modeling using Passive Sensing

TL;DR

WiFiMod is a Transformer-based model that leverages WiFi logs to accurately predict indoor human mobility across multiple spatial scales, addressing data scarcity and complex mobility patterns.

Contribution

It introduces a multi-scale, multi-modal Transformer approach that models indoor human mobility using passive WiFi sensing, significantly improving prediction accuracy over existing models.

Findings

Achieves at least 10% higher prediction accuracy than state-of-the-art models.

Effectively captures long-term dependencies and multi-scale periodicity in indoor mobility.

Enables practical applications like hotspot prediction, indoor mobility simulation, and personal assistants.

Abstract

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.