Analyzing and forecasting service demands using human mobility data: A two-stage predictive framework with decomposition and multivariate analysis

Accurate service demand forecasts at critical facilities are fundamental for efficiently managing resources and promptly providing essential services to people and community. However, it has received little attention in the literature, mainly due to the unavailability of granular data and the lack of sophisticated forecasting methods. To address this gap, we provide a new perspective on sensing service demands at critical facilities leveraging fine-grained human mobility data, and propose a novel data-driven framework to forecast mobility patterns at the neighborhood level. Specifically, we develop a two-stage forecasting scheme to manage large-scale and complex human movement information. The first stage is to decompose the large-scale mobility data into spatial and temporal patterns, whereas the second stage is to model complex temporal dynamics using multivariate time series analysis. The proposed framework is implemented using real human mobility data obtained from mobile phone users. The results show that our model demonstrates the best predictive performance for varying forecast horizons, when compared to multiple benchmark methods including traditionally-used statistical and deep learning models. We also performed model robustness checks, showing that the proposed model is robust in making short-term and long-term forecasts. The proposed predictive framework could help businesses and local governments accurately forecast service demands for critical facilities for better allocating their resources.