AI-Powered CPS-Enabled Vulnerable-User-Aware Urban Transportation Digital Twin: Methods and Applications

TL;DR

This paper discusses methods for creating AI-powered digital twins for urban traffic management, emphasizing prediction and decision-making capabilities to improve transportation systems.

Contribution

It introduces a comprehensive framework integrating AI with cyber-physical systems for urban transportation digital twins, highlighting challenges and opportunities.

Findings

Digital twins enhance urban traffic management.

AI integration improves prediction and decision-making.

Framework guides multidisciplinary development.

Abstract

We present methods and applications for the development of digital twins (DT) for urban traffic management. While the majority of studies on the DT focus on its ``eyes," which is the emerging sensing and perception like object detection and tracking, what really distinguishes the DT from a traditional simulator lies in its ``brain," the prediction and decision making capabilities of extracting patterns and making informed decisions from what has been seen and perceived. In order to add value to urban transportation management, DTs need to be powered by artificial intelligence and complement with low-latency high-bandwidth sensing and networking technologies, in other words, cyberphysical systems. This paper can be a pointer to help researchers and practitioners identify challenges and opportunities for the development of DTs; a bridge to initiate conversations across disciplines; and a road map to exploiting potentials of DTs for diverse urban transportation applications.