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  Scenario Selection

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  Map Overview

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  Statistics II

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  Statistics I

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  Performance Boxplot I

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  Performance Boxplot II

UNDER

Urban Navigation for Driverless and Excellent Routing

🚀 Inspiration

Efficient vehicle scheduling is critical to improving customer satisfaction and optimizing resource utilization in urban environments. Inspired by the challenges of real-world routing in busy cities, we set out to create a tool that ensures fast and reliable vehicle scheduling using advanced computational algorithms.

🛠️ What It Does

UNDER simplifies the passenger pick up and delivery to the Vehicle Routing Problem (VRP), which is then solved using the highly optimized CP-SAT solver.

• Algorithm with very good results (more than 30% better than random allocation)
• Scalable to over 50 taxis
• For huge fleets, UNDER uses a still very good (over 20% improvement) greedy approximation algorithm
• The optimization target can be changed even during the day, to prioritise speed, energy consumption or both

🧑‍💻 How We Built It

We combined cutting-edge technologies and tools to bring UNDER to life:

• Python: For backend logic and CP-SAT implementation.
  
• OR-Tools: To handle constraint-solving and optimization.
  
• Socket.IO: For real-time communication between the backend and frontend.
  
• React: To create an interactive and responsive user interface.
  
• OpenRouteService: For routing accurate to the street.
• Leaflet: To visualize maps and vehicle paths seamlessly.
  

🌟 Accomplishments We're Proud Of

• Speed and Reliability: Our approach still has very good results for huge amounts of taxis.
• Scalable Design: Built a flexible system capable of handling diverse routing challenges.
  
• User-Friendly Interface: Developed an intuitive interface for seamless interaction and visualization.
  

Built With

• ts