Real time location data derived from mobile applications is a powerful tool for addressing various urban challenges, including tourism planning, parking management, bus route optimization, and resource allocation. Besides, it offers invaluable insights for shaping strategic decisions in commercial domains such as location based services, market share analysis, and behavioral profiling. In this expansive study, we aim to address all of the aforementioned challenges by investigating the behaviors and patterns of smartphone users within urban environments, particularly in the domains of tourism, transportation, and retail. Our approach encompasses the development of a sophisticated data platform from inception to implementation, which includes the formulation of use cases, architectural design, and implementation of modules. We employ state of the art techniques and technologies, including data anonymization, ETL pipelines, and utilizing Google BigQuery and Vertex AI for data processing and machine learning model development. A modular architecture based on reusable analytical building blocks was developed to generate data products that support multiple stakeholder driven use cases. Additionally, we apply interactive data visualization techniques via Power BI to facilitate the effective interpretation of analytical findings by stakeholders. The developed models address a wide range of mobility analytics tasks, including mobility profiling, frequent trajectory mining, area of influence analysis, traffic anomaly detection, and origin destination pattern analysis. The results demonstrate the framework's ability to capture user mobility dynamics at fine spatial and temporal resolutions, providing actionable insights for urban planning and strategic business decision making.
Rethinking Urban Mobility Prediction: A Super-Multivariate Time Series Forecasting Approach
Sichuan University（四川大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；McGill University（麦吉尔大学）；Shanghai Jiao Tong University（上海交通大学）；Shanghai AI Lab（上海人工智能实验室）
Synergistic Fusion of Topological Structure and Temporal Semantics of Mobility for Urban Region Embedding
Urban AI Institute, Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院城市人工智能研究院）；Graduate School of Data Science, KAIST（韩国科学技术院数据科学研究生院）；Department of Industrial Engineering and Systems, KAIST（韩国科学技术院工业工程与系统系）；Department of Civil and Environmental Engineering, KAIST（韩国科学技术院土木与环境工程系）
Self-organized free-flight arrival for urban air mobility
Technische Universität Dresden（德累斯顿工业大学）；Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI)（可扩展数据分析与人工智能中心（ScaDS.AI））；Institute of Flight Systems, Universität der Bundeswehr München（慕尼黑联邦国防军大学飞行系统研究所）
GenAI-powered Multi-Agent Paradigm for Smart Urban Mobility: Opportunities and Challenges for Integrating Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) with Intelligent Transportation Systems
Oak Ridge National Laboratory（橡树岭国家实验室）；University of Washington（华盛顿大学）；Texas A&M University（德克萨斯农工大学）
