Travel Plan Assistant

Team

Table of Contents

  1. Introduction
  2. Solution Architecture
  3. Software Designs
  4. Testing
  5. Conclusion
  6. Links

Introduction

Travel planning is currently fragmented across multiple platforms, making structured itinerary creation a time-consuming process. Furthermore, optimizing time, cost, and routes is challenging without a unified system for automated travel planning. The Travel Plan Assistant is a web-based solution designed to automatically generate structured, optimized itineraries based on user inputs such as destination, time, and budget. By providing a personalized and intelligent solution, this application reduces manual effort and improves overall travel planning efficiency for tourists, business travelers, and budget-conscious individuals.

Solution Architecture

The system follows a client-server architecture. The frontend is a React-based web application that handles user interactions and provides input flexibility. This frontend communicates via JSON REST APIs to a Node.js and Express backend server, which manages the core business logic and processes requests. Data is persistently stored and retrieved using a MySQL relational database. To enable algorithmic route optimization, the backend integrates with an External Routing API (such as Google Maps) to fetch real-time distance and travel time data via HTTP requests.

Software Designs

The database design relies heavily on Graph-Based Data Modeling to facilitate automated route optimization. In the Entity-Relationship schema, destinations are mapped as nodes, while transport routes act as the edges connecting them. An ITINERARY_DESTINATION mapping table resolves the many-to-many relationship between users’ travel plans and selected destinations, ensuring the exact visit order is maintained for each generated plan.

Testing

Testing focuses on verifying end-to-end functionality and handling edge cases within the multi-criteria itinerary generation process. ]Key non-functional requirements such as performance, scalability, maintainability, and security are evaluated. Special emphasis is placed on validating API reliability and data accuracy when retrieving distance and pricing metrics from external services.

Conclusion

The MVP successfully demonstrates the feasibility of automated travel planning using graph-based algorithms and real-world routing data. By streamlining complex travel decisions and optimizing routes, the system significantly enhances the user’s travel experience. This scalable foundation validates the use of APIs to reduce manual planning and will be expanded in future developments to include more flexible transport choices and advanced optimization algorithms.