StingerOps: Smarter Campus Transit

Inspiration

Every Georgia Tech student knows the pain of relying on the Stinger buses. Walking from MARTA to the Paper Tricentennial in the middle of Atlanta’s summer heat is nothing short of cruel and unusual punishment.

We decided enough was enough—it’s time to re-imagine campus transportation so that no student has to suffer from inefficient routes again.

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What It Does

StingerOps analyzes data on student movement patterns (class times, campus hotspots, and population density) and uses optimization algorithms to generate smarter bus routes.

Instead of fixed, inefficient routes, we propose dynamic and data-driven routes that minimize travel time and maximize coverage.

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How We Built It

• Frontend (React):
  We borrowed an interactive map from the google maps api that updates in real time to show optimized routes.
  
• Backend (Django + Python):
  
  • Used pandas to process CSV datasets containing student schedules and building locations.
    
  • Applied optimization techniques to determine the most efficient bus paths.
    
  • Packaged results into a REST API with Django to serve route recommendations directly to the frontend.
    

In short, students click → backend optimizes → frontend updates.

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Challenges We Ran Into

• Actually optimizing the bus routes. Route optimization is a hard problem (similar to the Traveling Salesman Problem, which is NP-hard).
  
• Balancing efficiency vs. fairness—it’s easy to optimize for a subset of students, but much harder to ensure everyone benefits.
  

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Accomplishments We’re Proud Of

• Successfully integrating real route optimization results with our frontend.
  
• Creating a user-friendly front page that makes the project feel polished and practical.
  

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What We Learned

• Never create your own dataset if you can avoid it.
  
  • Data collection is time-consuming and messy.
    
  • Pre-existing data saves time for what really matters: building solutions.
    
• How to bridge frontend and backend efficiently in a hackathon setting.
  

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What’s Next for StingerOps

• Adding a feature for administrators to define custom stops and generate optimized routes automatically.
  
• Scaling to handle real-time demand—routes that adapt based on current student flows (e.g., MARTA rush hours vs. class change periods).
  
• Integrating with GT’s existing transit systems for real-world deployment.
  

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Built With

• django
• google-maps
• google-route-optimization-api
• python
• react