To develop an AI-powered data pipeline that processes real-time traffic sensor and camera data to detect congestion patterns, optimize traffic flow, and provide predictive analytics for smart city infrastructure management.
Study smart city data architecture and IoT integration.
Simulate traffic sensor data streams.
Ingest data using Kafka or cloud streaming services.
Store raw data in a distributed data lake.
Implement Spark streaming for real-time aggregation.
Apply AI models for traffic congestion prediction.
Perform time-series analysis on traffic patterns.
Build dashboards for real-time traffic visualization.
Optimize pipeline latency and scalability.
Implement anomaly detection for accidents.
Integrate alert system for congestion zones.
Secure data transmission channels.
Conduct scalability testing.
Document AI integration and performance metrics.
