TransitDash: Ridership Data Analysis

📖 Overview

This project provides a Python Dash dashboard designed to help the my former work's transportation office analyze ridership trends across their campus shuttle routes. By visualizing historical and real-time ridership data, this tool supports data-driven decisions such as:

• Adjusting vehicle capacity
• Modifying service hours
• Identifying under- or over-utilized stops

Additionally, the project required (but not included here) scripts for data cleaning and reformatting, tailored to the vendor's data inconsistencies, ensuring that the dashboard receives well-structured, accurate inputs.

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Getting Started

Prerequisites

Before using this dashboard, ensure you have the following installed:

• Python (v3.10+ recommended)

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Directory Structure

TransitDash/
│-- data/ # Raw and processed datasets
│-- docs/ # Documentation and assets for the README
│-- pages/ # Application pages (Dash)
│-- static/ # Static assets (CSS, JS, images)
│-- utils/ # Utility scripts for data cleaning and processing
│-- run.py # Entry point to launch the dashboard
│-- README.md # Project documentation

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Dashboard Features

Time-Based Ridership Analysis

The Time page allows you to explore ridership across multiple granularities. Available visualizations include:

• Ridership by semester and year
• Ridership by month, week, and date
• Ridership in 30-min intervals
• Ridership by scheduled time at any selected stop

By default, the dashboard displays ridership trends for the entire date range defined by the filters at the top of the page.

Stop Utilization

The Stop Utilization page highlights which stops are most and least frequented. It includes:

• Top and bottom 10 stops by ridership (adjustable in code)
• Daily ridership breakdowns for each day of the week across these top and bottom stops

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Future Improvements

This project is under active development. Upcoming enhancements include:

• Redesigned and more visually appealing home page
• Enhanced Ridership Summary page with at-a-glance overview
• Drag-and-drop data ingestion with automated preprocessing tailored to the company’s specific data format

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Usage

1. Clone the repository:

   git clone https://github.com/alexstvn/TransitDash.git
   cd TransitDash

2. Install dependencies

   pip install -r requirements.txt
   

3. Run the application.

   python run.py
   

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Contact

For questions or feedback, please feel free to reach out to me!