Infrastructure Systems

Human-in-the-loop Digital Twins for Smart Cities

Led by Jinwoo Jang, Ph.D.

PROJECT

This project aims to create data-enabled hyperlocal digital twins, advancing the understanding of how multiple people on streets interact with urban infrastructure and environments. The project will leverage heterogeneous sensor data that capture street-level micro-mobility (e.g., pedestrians, e-scooters, bikes) and their interactions with contextual surroundings (e.g., street objects, city infrastructure, traffic, air quality, temperature). The objectives of this project include developing 1) hyperlocal situation awareness through the data fusion of heterogeneous WPB datasets and multimodal multi-human behavior analytics, 2) virtual streetscape simulation environments based on real 3D map data, multimodal agents and traffic, and human-infrastructure interactions, 3) data-informed agent behavior modeling based on micro-mobility trajectories.

This project can ultimately improve the public safety, design, and sustainability of our streetscapes via digital twin technologies. The scientific contribution includes (1) the data fusion of heterogeneous data (e.g., point clouds, video, mobile sensing data) to advance the hyper localization of micro-mobility; (2) dynamic modeling of multimodal human behaviors with respect to contextual information (e.g., wayfinding based on nearby traffic and street objects); and (3) data-driven digital twin agents to advance the real-world representations in simulation environments. The impacts are significant since a data-driven understanding of spatiotemporal variability in agent/vehicle mobility patterns and the human-infrastructure interactions will contribute to advancing human-centric street morphology and design. Human-centric street environments will maximize urban livability, economic prosperity, and environmental sustainability.