Real-time city-scale traffic analytics requires processing 100s-1000s of CCTV streams under strict latency, bandwidth, and compute limits. We present a scalable AI-driven Intelligent Transportation System (AIITS) designed to address multi-dimensional scaling on an edge-cloud fabric. Our platform transforms live multi-camera video feeds into a dynamic traffic graph through a DNN inferencing pipeline, complemented by real-time nowcasting and short-horizon forecasting using Spatio-Temporal GNNs. Using a testbed to validate in a Bengaluru neighborhood, we ingest 100+ RTSP feeds from Raspberry Pis, while Jetson Orin edge accelerators perform high-throughput detection and tracking, producing lightweight flow summaries for cloud-based GNN inference. A capacity-aware scheduler orchestrates load-balancing across heterogeneous devices to sustain real-time performance as stream counts increase. To ensure continuous adaptation, we integrate SAM3 foundation-model assisted labeling and Continuous Federated Learning to update DNN detectors on the edge. Experiments show stable ingestion up to 2000 FPS on Jetson Orins, low-latency aggregation, and accurate and scalable ST-GNN forecasts for up to 1000 streams. A planned live demonstration will scale the full pipeline to 1000 streams, showcasing practical, cross-fabric scalability.

翻译：城市级实时交通分析需要在严格的延迟、带宽和计算限制下处理数百至数千路闭路电视视频流。本文提出一种可扩展的AI驱动智能交通系统，旨在解决边缘-云架构上的多维扩展问题。该平台通过DNN推理流水线将实时多路摄像头视频流转换为动态交通图，并辅以基于时空图神经网络的实时临近预报与短时域预测。通过在班加罗尔某社区搭建测试平台进行验证，系统从树莓派设备接收100余路RTSP视频流，由Jetson Orin边缘加速器执行高吞吐量检测与跟踪，生成轻量级流量摘要供云端图神经网络推理使用。容量感知调度器在异构设备间协调负载均衡，确保流数量增加时仍能维持实时性能。为实现持续自适应，系统集成SAM3基础模型辅助标注与持续联邦学习机制，用于更新边缘端的DNN检测器。实验表明：Jetson Orin设备可稳定接收高达2000 FPS的视频流，系统具备低延迟聚合能力，并为多达1000路视频流提供准确且可扩展的时空图神经网络预测。计划中的实时演示将把完整流水线扩展至1000路视频流，展示跨架构的实际可扩展性。