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arXiv · 2609.29223

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

Mayank Arya·Pranjal Naman·Priyanshu Pansari·Roopkatha Banerjee·Daksh Mehta·Manjil Nepal·Akash Sharma·Yogesh Simmhan
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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.

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Mayank Arya, Pranjal Naman, Priyanshu Pansari, Roopkatha Banerjee, Daksh Mehta, Manjil Nepal, Akash Sharma, Yogesh Simmhan. 2026-09-24. TrafficFab: An Autonomic Edge-Cloud Testbed Fabric forAI-Driven Traffic Management. https://arxiv.org/abs/2609.29223

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