DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
(ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING) AND
COMPUTER SCIENCE AND ENGINEERING (CYBER SECURITY)
CIP67/CYP67: MINI PROJECT TERM: March - June 2025
PROJECT SYNOPSIS
Title of the Project
AI-Powered Travel Planner with Smart Packing Assistant
PROJECT TEAM MEMBERS
Sl. No USN Name
01 1MS22CI014 Appu Appannagol
02 1MS22CI401 Bharath C K
03 1MS22CI001 Abdulappa
M.S. RAMAIAH INSTITUTE OF TECHNOLOGY
(Autonomous Institute, Affiliated to VTU)
Contents
Title of the Project:
AI-Powered Travel Planner with Smart Packing Assistant
Project Stream:
Application / Data Intelligence / Travel & Tourism Technology
Problem Statement:
Travel planning involves multiple factors such as budget, itinerary, accommodation, packing
essentials, and local recommendations. Manual trip planning is time-consuming, and travelers
often struggle with organizing their journey efficiently. This project aims to automate travel
planning using AI by generating personalized itineraries, smart packing lists, real-time flight and
hotel recommendations, and local activity suggestions based on user preferences and real-time
data.
Objective:
1. Develop an AI model that generates personalized travel itineraries based on user
preferences such as destination, budget, trip duration, and interests.
2. Implement a Smart Packing Assistant that recommends essential items based on weather
conditions, activities, and trip duration using OpenWeather API.
3. Integrate flight and hotel booking APIs (Skyscanner, Booking.com) to provide real-time
recommendations.
4. Implement local activity and restaurant suggestions using Google Places API.
5. Build a budget estimator that predicts and manages travel expenses.
6. Develop an interactive dashboard that provides users with a centralized platform to view
their travel plan, packing list, and recommendations.
Scope of the Project:
This project aims to streamline the travel planning process by integrating AI-driven solutions to
enhance user experience, reduce manual efforts, and provide personalized travel
recommendations. The system caters to a wide range of users, including frequent travelers,
business professionals, adventure seekers, and travel agencies, by offering customized itineraries,
smart packing lists, and budget-friendly recommendations. The incorporation of real-time flight
and hotel suggestions, weather-based packing assistance, and local activity recommendations
ensures an optimized and hassle-free travel experience.
What contribution to the society, would the project make?
This project contributes to society by:
1. Enhances travel experience by reducing manual trip planning efforts.
2. Reduces unnecessary packing, contributing to sustainable travel.
3. Encourages travelers to explore optimized budgets and better travel deals.
4. Supports accessibility by offering AI-powered recommendations tailored to user needs.
5. Contributes to eco-friendly tourism by promoting responsible travel habits.
6. Assists solo travelers by ensuring they have all necessary information for a safe journey.
7. Promotes cultural exploration by suggesting local experiences and off-the-beaten-path
attractions.
Hardware & Software to be used:
Hardware:
Development workstations with minimum 16GB RAM and multi-core processors
Cloud server infrastructure for hosting the application
Testing devices (laptops, mobile devices) for cross-platform verification
Software:
Programming Languages: Python, JavaScript
Frameworks: Flask / Node.js for Backend, React.js for Frontend
AI API: OpenAI GPT-4 for itinerary & packing list generation
Database: MongoDB / PostgreSQL for storing user preferences
Tools: Git, Docker, VS Code
Google Places API (location recommendations) and Skyscanner API (flight details)
Booking.com API (hotel listings) and OpenWeather API (weather-based packing list
generation)
M.S. RAMAIAH INSTITUTE OF TECHNOLOGY
(Autonomous Institute, Affiliated to VTU)
References
1. Chen, A., Ge, X., Fu, Z., Xiao, Y., & Chen, J. (2024). "TravelAgent: An AI Assistant for
Personalized Travel Planning." arXiv preprint arXiv:2409.08069.
2. de la Rosa, T., Gopalakrishnan, S., Pozanco, A., Zeng, Z., & Borrajo, D. (2024). "TRIP-
PAL: Travel Planning with Guarantees by Combining Large Language Models and
Automated Planners." arXiv preprint arXiv:2406.10196.
3. Ho, N. L., & Lim, K. H. (2022). "POIBERT: A Transformer-based Model for the Tour
Recommendation Problem." arXiv preprint arXiv:2212.13900.
4. Barua, B., & Kaiser, M. S. (2024). "Optimizing Travel Itineraries with AI Algorithms in
a Microservices Architecture: Balancing Cost, Time, Preferences, and Sustainability."
arXiv preprint arXiv:2410.17943.
5. "Majority of Americans Use AI for Travel Planning." (2024). Globetrender.
6. "Travel Recommendation Engine: Boost Bookings and Revenue": Kody Technolab
7. "TRIP-PAL: Travel Planning with Guarantees by Combining Large Language Models
and Automated Planners"
8. "Optimizing Travel Itineraries with AI Algorithms in a Microservices Architecture":
Balancing Cost, Time, Preferences, and Sustainability arXiv
Guide Comments:
Signature of the Guide with date
