T-axi Fleet

Inspiration

We set out to solve one of the most complex challenges in autonomous transportation: the Open Multi-Depot Vehicle Routing Problem (MDVRP) with Pickup and Delivery constraints. Our goal was to optimize the operations of autonomous taxi fleets by creating an intelligent dispatch and routing system that could efficiently handle real-time passenger requests across multiple depots.

What it does

T-axi Fleet is a comprehensive fleet management solution that:

• Optimizes the distribution and dispatch of autonomous taxis across multiple depots
• Processes real-time pickup and delivery requests while considering vehicle capacity and range constraints
• Balances workload across the fleet to maximize efficiency and minimize wait times
• Provides real-time monitoring and analytics for fleet operators
• Incorporates traffic conditions and dynamic routing to ensure optimal service delivery ## How we built it Mathematical modelling ## Challenges we ran into Complexity of Multi-Depot VRP: Balancing requests across multiple depots without overloading a single one. Integration Issues: Bringing together mapping, traffic data, and our routing algorithms was more challenging than anticipated. ## Accomplishments that we're proud of Built an intuitive dashboard for fleet managers to monitor operations effortlessly. ## What we learned Its best to model first and find existing solutions to similar problems. Then you can easily apply them to the specific use case. ## What's next for T-axi Fleet Extended metrics to enable an easy job for a fleet manager and

Built With

• angular.js
• python