Given the trend towards urbanization, understanding real-time human mobility in urban areas has become increasingly important for many research areas from Mobile Networking, to Transportation/Urban Planning, Behavior Modeling, Emergency Response, to recent Pandemic Mitigation. Many analytical models have been proposed to understand human mobility based on mobility data. However, most of these data are proprietary and cannot be accessed by the research community at large. Fortunately, based on the latest expansion of urban infrastructures, such mobility data has been collected by city government agencies and some companies that are willing to share the data for social good. However, a key challenge is the privacy concern since such data usually have sensitive information and system design details for potential privacy and security issues. To address this issue, the project aims to generate realistic yet synthetic mobility data through machine learning based on the real mobility data analytics and then share these realistic synthetic data with the research community. The objective of the project is to lower the entry barriers for interdisciplinary researchers in mobility data-intensive research aimed at addressing major scientific/societal challenges related to urban mobility.
The core merit of the project lies in integrating two aims, i.e., privacy-preserving data synthesis and data integration, for large-scale smart city mobility research. For the first research aim, the project plans to utilize recent advances in Generative Adversarial Networks (GANs) to enable large-scale mobility data synthesis. The goal is to achieve the individual-level release of realistic synthetic mobility data by GAN-based models targeting key characteristics of human mobility. The GAN architecture proposed has novel technical components to augment basic GAN frameworks, which optimize the fundamental trade-off between privacy (regarding removing/obfuscating sensitive mobility features) and utility (in terms of preserving non-sensitive mobility features) with long-range dependencies (in terms of repeated mobility patterns) revealed. For the second research aim, the PIs plans to perform multi-modal data integration based on aligned multi-tensor decomposition under mobility semantics. The technical approach proposed is to enable multi-modal data integration based on synthetic single-modal data for comprehensive mobility modeling with a set of machine learning techniques including novel mobility semantic learning and multi-tensor decomposition with aligned spatiotemporal granularity.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
