Travel demand generates large amounts of data related to routes, travel time, seasonal patterns, and transport modes. Analyzing this data using traditional methods such as manual reports or basic statistics often fails to provide clear insights into traveler behavior and route performance, especially when dealing with large datasets. This project addresses the problem by transforming complex travel data into interactive visual dashboards. Using Python libraries such as Pandas and NumPy for data processing and Plotly for visualization, the system presents travel trends through charts and route-based analytics. These visualizations help identify route popularity, seasonal demand patterns, and traveler preferences, enabling better insights and supporting more informed transportation planning decisions.

This is a hands-on project used to understand the concept of Exploratory Data Analysis (EDA) and learn as well as implement the practical use of various relevant python libraries.