A small logistics provider needs a smart solution to determine the best shipping route for cargo traveling across borders using multi-modal transportation (land, air, and sea).
Our solution: Multi-Modal Cross-Border Route Selector – an intelligent logistics system that provides optimized shipping routes based on factors like:
✅ Cost Efficiency
✅ Transit Time Optimization
✅ Cross-Border Feasibility
✅ Capacity & Perishability Constraints
| Name | Role | Responsibilities |
|---|---|---|
| Toheed Akhtar | 🚀 Team Leader | Architect of Core Logic, ML Developer |
| Pratibha Singh | 🎨 Frontend Developer | UI/UX Design, Frontend Implementation |
| Raghavendra Baheti | 🔧 Backend & Deployment | Server Management, Infrastructure |
| Akash Soni | 🛠️ Backend, DataBase & Deployment | DataBase Development, System Deployment |
- User Input: Origin, destination, cargo details, and priority (cheapest vs. fastest).
- Route Calculation: Using Dijkstra’s Algorithm, we generate multi-modal routes with cost & time estimates.
- Ranking & Optimization: Routes are ranked based on efficiency, allowing users to choose the best option.
- Output: JSON response for easy front-end integration.
- Frontend: React, JavaScript
- Backend: Python, Flask
- DataBase: MySQL
- Algorithms: Graph Theory, Dijkstra’s Algorithm
- Infrastructure: Kubernetes
- Libraries: NetworkX
- Graph-Based Route Modeling (Nodes = Cities, Ports, Airports | Edges = Transport Routes)
- Cost & Transit Time Estimation
- Multi-Modal Optimization (Land, Air, Sea)
- User-Friendly Interface
- API for Integration
This project was developed as part of LogiTHON 2024. Tech Transit worked together to create this innovative logistics solution.
Our project is containerized and available on Docker Hub:
- Frontend Image: Frontend Docker Hub Link
- Backend Image: Backend Docker Hub Link
This project is licensed under the MIT License – see the LICENSE file for details.
