The field of intelligent transportation systems is rapidly evolving, with a focus on leveraging computer vision, machine learning, and edge computing to improve road safety, traffic management, and infrastructure maintenance. Recent developments have seen the application of innovative techniques such as vision transformers, generative adversarial networks, and physics-informed joint generative learning to tackle complex challenges like real-time pothole detection, traffic analytics, and autonomous vehicle navigation. Noteworthy papers in this area include: Enhancing Road Safety Through Multi-Camera Image Segmentation with Post-Encroachment Time Analysis, which presents a novel framework for real-time safety assessment at signalized intersections. A Novel AI-Driven System for Real-Time Detection of Mirror Absence, Helmet Non-Compliance, and License Plates Using YOLOv8 and OCR, which demonstrates a practical solution for automated traffic rule enforcement. SAE-MCVT: A Real-Time and Scalable Multi-Camera Vehicle Tracking Framework Powered by Edge Computing, which achieves real-time operation on 2K 15 FPS video streams and maintains an IDF1 score of 61.2.