Deep Learning–Reinforced Self-Adaptive CI/CD Framework for Traffic-Aware Deployment in Intelligent Transportation Systems

Intelligent Transportation Systems (ITS) generate large volumes of real-time traffic data that require accurate forecasting, timely anomaly detection, and reliable deployment strategies to maintain operational efficiency. Existing ITS solutions often treat prediction, anomaly detection, and system deployment as separate processes, resulting in limited adaptability and increased deployment risk in dynamic traffic environments. To address this gap, this paper proposes a Deep Learning–Reinforced Self-Adaptive CI/CD Framework that integrates traffic forecasting, anomaly detection, and adaptive deployment strategies within a unified DevOps pipeline using GitLab and Ansible. The framework employs a sequence-to-sequence LSTM for short-term traffic forecasting and an LSTM autoencoder for anomaly detection, both embedded within a closed-loop CI/CD pipeline enhanced by reinforcement learning for deployment optimization. Experiments on the Metro Interstate, PeMSD7_228, PeMSD7_1026, and a combined heterogeneous dataset show that the proposed framework consistently outperforms statistical and deep learning baselines. It achieves up to 21.8% reduction in MAE and 19.0% reduction in RMSE compared with LSTM baselines, with a best RMSE of 20.5 on the Metro dataset and 22.2 on the combined dataset. The anomaly detection module achieves an AUROC of 0.94 and reduces detection delay to 6.4 seconds, outperforming GRU-AE and Isolation Forest baselines. The adaptive deployment mechanism reduces Change Failure Rate (CFR) to 4.3% and rollback rate to 3.1%, while improving system availability to 98.6% and reducing Mean Time to Recovery (MTTR) to 11.3 minutes. These results demonstrate that embedding AI-driven analytics into CI/CD workflows improves both predictive intelligence and deployment resilience, providing a scalable and self-adaptive solution for real-time ITS monitoring.