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Cross-zone and extreme-aware mobility learning of crowd interactions with built environments

We propose an Adaptive Crowd mobility analytics system based on Cross-zone Interactive learning (ACroCI) to capture, interpret, and forecast how the mobility of human crowds interacts with built or man-made environments (e.g., building functions, event occurrences, and changes in the crowd sensing infrastructures). We have conducted a large-scale real-world case study of ACroCI by leveraging the collective and anonymized association data harvested from the campus Wi-Fi access points (APs), to understand how the interactions affect the forecast of crowdflows. We have analyzed the large-scale Wi-Fi association data and derived adaptive learning and data-driven designs on important crowd mobility features like multi-level co-flows and local-global cross-zone interactions. ACroCI accounts for these interactive features and adaptively learns the multi-scale crowd distributions with a novel capsule neural network augmented by interactive attention routing, and accurately predicts the arrivals at, and departures from, each AP. Strengthened by multi-task extreme-aware learning and efficient data imputation, ACroCI further adapts to extremeflows when the crowds interact with events, and initializes its model for altered APs. Our extensive experimental evaluations with >4.8 × 107 association data from two large universities have corroborated the accuracy, adaptivity, robustness, and effectiveness of ACroCI in forecasting the crowd interactions with the man-made environments (in terms of crowd flows), achieving a >20% accuracy improvement over the other state-of-the-arts.