AI Cloud Platform for Smart City Traffic Monitoring and Analysis Using Multi-Dimensional Big Data and Deep Learning Models

Rapid urbanization and persistent road-safety challenges have increased the demand for intelligent traffic management systems capable of supporting traffic monitoring, incident analysis, and short-term forecasting. Existing artificial intelligence solutions frequently address these functions through separate, task-specific applications, making it difficult for traffic operators to access heterogeneous analytical services within a common operational environment. This study presents a cloud-based, multi-source traffic intelligence platform that incorporates independent analytical services for CCTV video, UAV video, and IoT sensor data. The proposed platform performs platform-level service integration among the three sources. Its principal analytical component is a multi-model, multi-task UAV video framework consisting of three task-specific pipelines for accident detection and classification, emergency-vehicle detection, and vehicle tracking, counting, and congestion analysis. The semantic outputs of these pipelines are integrated at the decision level to generate annotated videos, traffic statistics, congestion assessments, incident alerts, and consolidated traffic reports. YOLOv8-based models are used for traffic-object and incident detection, Deep SORT is employed for multi-object tracking, and DCRNN is independently applied to PeMS-BAY sensor data for short-term traffic forecasting. A graph-partitioning strategy with overlapping boundary nodes is also evaluated to investigate the trade-off between forecasting accuracy and computational efficiency. Module-level experiments demonstrate effective task-specific performance. The CCTV accident detection model achieves a validation precision of 89.09%, recall of 88.10%, and mAP of 88.09%. The augmented UAV accident-analysis model achieves a validation precision of 82.20% and mAP of 80.20%. For five-minute traffic forecasting, the DCRNN service obtains an MAE of 0.85, MAPE of 1.63%, and RMSE of 1.54 on the PeMS-BAY dataset. These results demonstrate the feasibility of integrating independently processed traffic-analysis services within a common cloud platform while using multi-model UAV analytics to provide consolidated aerial traffic intelligence.