TrafficFab: An Autonomic Edge-Cloud Testbed Fabric forAI-Driven Traffic Management

2026-09-24

AI Summary

This work proposes an autonomous edge-cloud collaborative architecture to address the latency, bandwidth, and energy constraints inherent in real-time traffic analysis across thousands of video streams in megacities. The framework integrates RTSP stream simulation for decentralized data acquisition and constructs a closed-loop decision system through heterogeneous edge DNN inference, cloud-based spatiotemporal graph neural network prediction, and large model-assisted federated learning. This design supports energy-aware elastic scaling at the edge and dynamic scheduling in the cloud. Validated in a Bangalore urban scenario, the platform stably sustains the analysis of approximately 400 concurrent real-time video streams, demonstrating its practicality and scalability for city-scale intelligent transportation systems.

Abstract

Traffic management in emerging megacities requires real-time analytics over thousands of CCTV video streams under latency, bandwidth, compute and energy constraints. We present TrafficFab, an autonomic edge--cloud testbed for AI-driven traffic management, designed to validate a representative slice of a megacity deployment. TrafficFab combines RTSP stream emulation, heterogeneous edge inference using DNNs, cloud-based nowcasting and forecasting using Spatio-Temporal Graph Neural Network (ST-GNN), and continual model adaptation through foundation-model (FM)-assisted Federated Learning (FL). Its autonomic control enables fine-grained scale-out/in of edge inference through energy- and migration-aware scheduling, elastic scale-up/down of GNN forecasting on public clouds, and periodic adaptation of the DNN on edge accelerators and private cloud, without centralized video collection. We evaluate TrafficFab on a Bangalore-city inspired deployment, spanning Raspberry Pis, Jetson accelerators, GPU fogs, private cloud servers, and cloud VMs, sustaining real-time analytics for $\approx 400$ live camera streams (10% of Bangalore) and analytically characterize larger setups. The results demonstrate that TrafficFab offers a practical validation-scale platform for closed-loop traffic analytics, short-term operational decision support, and longer-horizon planning analyses in megacity scales.

Problem

• Traffic Management
• Edge-Cloud Computing
• Real-time Video Analytics
• Megacity
• Resource Constraints

Innovation

• Edge-Cloud Testbed
• Spatio-Temporal Graph Neural Network
• Federated Learning
• Autonomic Control
• Traffic Management