This project aims to analyze traffic data using real-time and historical analytics techniques. The objective is to identify congestion patterns, peak traffic hours, and anomalies, supporting intelligent transportation planning and traffic management decisions.
Collect real-time and historical traffic datasets from public or simulated sources.
Clean and preprocess streaming and batch data.
Perform exploratory analysis on traffic volume and speed metrics.
Identify peak-hour congestion patterns using statistical analysis.
Apply clustering techniques to group traffic behavior patterns.
Detect anomalies such as accidents or abnormal congestion.
Visualize traffic patterns using time-series and spatial charts.
Analyze results to recommend traffic optimization strategies.
Evaluate system scalability and performance.
Document analytical workflows, tools, and findings comprehensively.
