Edge AI-Based Real-Time Incident Detection and Emergency Response in Intelligent Transportation Systems

Abstract

ABSTRACT Intelligent Transportation Systems (ITS) rely heavily on rapid, automated incident detection to mitigate traffic congestion, reduce secondary collisions, and minimize emergency dispatch delays. Centralized cloud-centric architectures suffer from high backhaul network latency, excessive bandwidth consumption, and privacy vulnerabilities. To overcome these limitations, Edge Artificial Intelligence (Edge AI) has emerged as a transformative paradigm by shifting local computer vision inference and multisensor fusion directly onto roadside edge infrastructure. This review paper provides a technical analysis of state-of-the-art Edge AI frameworks for real-time incident detection and emergency response. We evaluate lightweight deep neural network models— such as YOLOv8-Nano, MobileNetV3, and Edge Vision Transformers— deployed on low-power embedded accelerators including NVIDIA Jetson Orin Nano and Google Coral TPU. Furthermore, we examine the integration of Cellular Vehicle-to-Everything (C-V2X) direct communication, priority signal preemption, and privacy-preserving edge architectures. Empirical benchmark comparisons demonstrate that localized edge inference drastically reduces total decision latency from over 340 milliseconds in cloud systems down to sub-35 milliseconds. Key technical challenges, environmental degradation factors, thermal constraints, and a prospective research roadmap for next generation resilient transportation infrastructure are systematically detailed.

KEYWORDS: Edge Artificial Intelligence, Intelligent Transportation Systems (ITS), Real-Time Incident Detection, Emergency Response Dispatch, Deep Learning at Edge, Computer Vision, C-V2X Communication, Roadside Units (RSU).