Traffic Pattern Analysis Using Real-Time and Historical Data Analytics

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.