PI: Sean Qian
Co-PI(s): N/A
University: Carnegie Mellon University
Industry partner: Fujitsu Research of America, Inc.
Our transportation infrastructure/service evolves from one of paved roads, bus routes, and concrete bridges, to the one that now includes a digital twin where sensors, data, software, and algorithms are deployed to closely monitor, predict, and manage its physical counterpart, thanks to the technologies in communication, sensing, and AI. We are moving to a future of transportation–the digital twin–that uses technology and innovation to advance future mobility that is safer, greener, smarter, and more equitable. The goal of this project is to work closely with key deployment partners to develop technologies for Digital Twin, particularly for the mobility systems using the Southwestern PA region as an example. It can be employed to improve public agencies’ decision-making, as well as to offer innovative service products to businesses and individuals. A digital twin platform builds on top of traditional mesoscopic traffic simulations but offers more heterogeneity and granularity in modeling human behavior and vehicular/passenger flow. The fundamental technique is to leverage AI techniques to replicate real-world infrastructure, service, and network flow using multi-source multi-modal data. For this research, we particularly looked into building a digital twin platform for Southwestern Pennsylvania, focusing on mobility systems of multiple modes.