video coding machine architecture for smart urban traffic optimization with deep learning

Intelligent transportation systems (its) are essential for modern urban infrastructure but grapple with real-time processing of voluminous traffic video data amid bandwidth and latency limitations. this paper introduces a novel video coding machine (vcm) architecture that synergistically combines versatile video coding (vvc) with an adaptive bitrate optimization algorithm—driven by neural features—and a hybrid convolutional neural network (cnn)–recurrent neural network (rnn) model for optimized compression and congestion prediction. the vvc core, enhanced by dynamic quantization parameter (qp) adjustments, minimizes data volume while upholding perceptual quality, whereas the cnn extracts spatial features (e.g., vehicle density) and the rnn captures temporal dynamics for precise forecasting. evaluated on diverse real-world datasets (cityscapes, bdd100k, tehran traffic), the system attains 94% prediction accuracy (with 93% precision and 95% recall), 60% data reduction, and 25% faster processing versus baselines like h.264/avc and h.265/hevc. this framework delivers a scalable, efficient solution for smart cities, fostering real-time its applications, substantial cost efficiencies in storage/transmission, and improved urban mobility/safety. by bridging advanced compression and deep learning, it advances sustainable traffic management paradigms.