AggieCommute

AggieCommute: Smarter, greener, data-driven commuting at UC Davis.

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Inspiration

AggieCommute was inspired by the daily challenges UC Davis students face when commuting across campus — especially with packed buses, full parking lots, and limited evening service. With Unitrans GTFS data and the TAPS Parking Impact Map both publicly accessible, we saw an opportunity to use open data to make transportation smarter, more efficient, and more sustainable for the UC Davis community.

What it does

AggieCommute analyzes Unitrans GTFS and TAPS parking data to uncover:

  • When and where campus buses operate most heavily
  • Which parking lots fill up earliest
  • Where there are mismatches between transit coverage and commuter demand

It presents these insights through:

  • Interactive visualizations (bar charts, stacked charts, weighted heatmap)
  • A clean, responsive web UI
  • A heatmap page that highlights service concentration by stop activity

How I built it

  • Backend/Data: I parsed and joined GTFS files (routes.txt, trips.txt, stop_times.txt, and stops.txt) using Python and Pandas. Data was visualized with Matplotlib and Folium.
  • Frontend/UI: I built the user interface using Next.js and Tailwind CSS, with separate pages for the landing overview, route visualizations, and a dedicated heatmap page.
  • Heatmap: I created a weighted stop heatmap based on the number of scheduled trips per stop, segmented by time of day.
  • Parking Analysis: I manually reviewed the TAPS Parking Impact Map to log average congestion times for campus lots and linked these insights to nearby Unitrans routes.

Challenges I ran into

  • Understanding how to join multiple GTFS tables properly and handle null/missing values
  • Dealing with inconsistent or sparse arrival_time data in the stop times file
  • Embedding large HTML-based Folium maps cleanly in the Next.js frontend
  • Designing a responsive UI layout that presents complex data clearly

Accomplishments that I am proud of

  • Turning raw GTFS data into meaningful insights and polished visualizations
  • Deploying a responsive, multi-page UI with clear navigation and user flow
  • Creating a weighted heatmap that highlights service intensity across campus
  • Aligning visual insights with real-world parking congestion to inform planning decisions
  • Successfully used Folium for the first time to generate and embed interactive geographic heatmaps

What I learned

  • How GTFS data is structured and how public transit systems are modeled
  • How to use Python to manipulate and visualize real-world transportation datasets
  • How to create dynamic Next.js pages with Tailwind and embed maps and charts
  • How to communicate data insights clearly through design and user experience

What's next for AggieCommute

  • Incorporate weekend service comparison to highlight weekend/weekday discrepancies
  • Add filters to the heatmap by time or route for more detailed analysis
  • Work with UC Davis TAPS or Unitrans to share findings and suggest improvements
  • Explore real-time bus arrival integration to support future route optimization

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