MTA Transit Analytics: Multi-Dimensional Data Modeling & Performance Dashboard

Project Overview:

This project focuses on building a robust data model and interactive analytical solution to evaluate the Metropolitan Transportation Authority (MTA) performance. Using the "cleaned_mta_data مستقل.csv" dataset, I developed a comprehensive data model (Power BI/Tableau) that integrates multiple transit streams—including Subways, Buses, LIRR, and Metro-North—into a unified analytical framework.

Key Technical Features:

Advanced Data Modeling: Structured a complex data model to handle time-series transit data, enabling seamless filtering and drill-down capabilities across different transportation modes.

Trend & Recovery Analysis: Engineered specific metrics to track ridership recovery percentages and daily averages, specifically monitoring the impact of the pandemic from March 2020 onwards.

Automated Data Cleaning: Processed raw transit records into a "cleaned" format to ensure data integrity and visualization accuracy.

Visual Insights: Created high-impact dashboards that visualize ridership trends, mode-specific drops (e.g., the 80% decline in Metro-North), and overall transit health indicators.

This project demonstrates the ability to transform raw, fragmented transportation data into an optimized data model that provides actionable insights for urban mobility and infrastructure planning.