CDS&E: Collaborative Research: Private Data Analytics Synthesis, and Sharing for Large-Scale Multi-Modal Smart City Mobility Research

鉴于城市化的趋势，了解城市地区的实时人员流动对于许多研究领域变得越来越重要，从移动网络到交通/城市规划，行为建模，应急响应，到最近的大流行缓解。人们提出了许多基于流动性数据的分析模型来理解人类的流动性。然而，这些数据大多是专有的，不能被研究界广泛访问。幸运的是，基于最近城市基础设施的扩张，这些交通数据已经被城市政府机构和一些愿意为社会公益分享数据的公司收集起来。然而，一个关键的挑战是隐私问题，因为这些数据通常包含敏感信息和潜在隐私和安全问题的系统设计细节。为了解决这个问题，该项目旨在通过基于真实移动数据分析的机器学习生成真实而合成的移动数据，然后与研究社区共享这些真实的合成数据。该项目的目标是降低跨学科研究人员在交通数据密集型研究中的进入门槛，旨在解决与城市交通相关的重大科学/社会挑战。该项目的核心优点在于，将保护隐私的数据合成和数据集成两个目标整合在一起，用于大规模的智慧城市移动研究。对于第一个研究目标，该项目计划利用生成对抗网络（GANs）的最新进展来实现大规模移动数据合成。目标是通过基于gan的模型，针对人类移动性的关键特征，实现个人层面的真实合成移动性数据发布。提出的GAN架构具有新颖的技术组件来增强基本GAN框架，优化了隐私（关于删除/混淆敏感移动特征）和实用性（关于保留非敏感移动特征）之间的基本权衡，并揭示了远程依赖关系（关于重复移动模式）。对于第二个研究目标，pi计划在移动语义下进行基于对齐多张量分解的多模态数据集成。提出了一种基于综合单模态数据的多模态数据集成的技术方法，该方法采用了一套机器学习技术，包括新颖的移动性语义学习和具有对齐时空粒度的多张量分解。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.