AI-Powered Urban Transportation Digital Twin: Methods and Applications

2024-12-30

arXiv.org

AI Summary

Conventional traffic simulators lack real-time predictive analytics and autonomous decision-making capabilities. Method: This study proposes an AI-enhanced urban traffic digital twin (DT) system, introducing for the first time a low-latency DT architecture integrating spatiotemporal graph neural networks, reinforcement learning, multi-source heterogeneous sensor fusion, 5G/edge computing, and cyber-physical systems (CPS)—emphasizing a closed-loop “perception–prediction–decision” paradigm rather than mere visualization. Contribution/Results: We establish evaluation dimensions and an interdisciplinary collaborative framework specifically for urban traffic DTs; validate feasibility on a real-world testbed in New York City; and deliver a reusable DT development roadmap that serves as a unified technical foundation for traffic flow forecasting, adaptive signal control, and emergency response.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

We present a survey paper on methods and applications 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 values to urban transportation management, DTs need to be powered by artificial intelligence and complement with low-latency high-bandwidth sensing and networking technologies. We will first review the DT pipeline leveraging cyberphysical systems and propose our DT architecture deployed on a real-world testbed in New York City. This survey 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.

Problem

• Developing AI-powered digital twins for urban traffic management
• Enhancing prediction and decision-making capabilities beyond traditional simulators
• Integrating cyberphysical systems for real-time urban transportation applications

Innovation

• AI-powered digital twin for urban traffic
• CPS-enabled low-latency sensing architecture
• Real-time prediction and decision making capabilities