HumoNet: A Framework for Realistic Modeling and Simulation of Human Mobility Network

• Published in: 2024 25th IEEE International Conference on Mobile Data Management (MDM) pp. 185 - 194
• Main Authors: Kim, Joon-Seok, Thakur, Gautam Malviya, Amichi, Licia, Burger, Annetta, Gunaratne, Chathika, Tuccillo, Joseph, Hauser, Taylor, Bentley, Joseph, Sparks, Kevin, De, Debraj, Brown, Chance, McBride, Elizabeth, McGaha, Jesse, Gaboardi, James, Nie, Xiuling, Christopher, Steven Carter
• Format: Conference Proceeding
• Language: English
• Published: IEEE 24.06.2024
• Subjects: Accuracy; Adaptation models; Analytical models; Benchmark testing; Computational modeling; Data models; data-driven; microscopic traffic simulation; patterns-of-life; point-of-interest; population; simulation; Transportation
• ISSN: 2375-0324
• DOI: 10.1109/MDM61037.2024.00042

Understanding, analyzing, and predicting human mobility and dynamics are valuable to solving pressing problems, developing effective plans, and prescribing timely remedies. As a computational approach, realistic human mobility simulations allow us to understand, analyze, and predict complex systems, including human societies. Accurate simulations rely on (1) the model that captures interactions and behaviors of myriad entities in our society and (2) the mapping of model instances to real-world entities. Taking this into account, this paper introduces the Human Mobility Network simulation framework (HumoNet), an integrated patterns of life (POL) simulation framework that leverages real-world data layers including transportation networks, points of interest, populations, popularity, and human trajectories. HumoNet is a data informed model in which agents are equipped with activities, locomotion, and planning capabilities. To simulate realistic kinematic maneuvers of individuals in transportation networks, HumoNet harnesses a microscopic traffic simulator that provides interaction among vehicles and traffic objects. In this paper, we describe the framework, outline our methodologies, and discuss the data processing and challenges of each data layer. Through experiments, we demonstrate that our simulations capture key features of human mobility by comparing them to the literature and real data using standard measures of human mobility (i.e., the radius of gyration, number of locations visited, level of exploration) and metrics scoring (i.e., Jensen-Shannon divergence). We envision that the synthetic data produced by HumoNet will serve as a benchmark for analyzing epidemics, deploying EV charging networks, and validating AI/ML tasks such as location prediction.