Travel-Related Web Application for Capturing Trip Information Using MERN with Integrated AI Assistant

This paper presented the design and development of a travel-related web application aimed at simplifying trip planning and management using the MERN stack integrated with an AI-powered assistant. The proposed system allows users to generate personalized travel itineraries by providing simple inputs such as destination, budget, duration, and travel preferences. The integration of artificial intelligence enhances the overall user experience by providing real-time suggestions, automated itinerary generation, and interactive assistance. The application offers a centralized platform where users can efficiently plan, organize, and manage their trips, reducing the need for manual research and fragmented tools. The system demonstrates how combining modern web technologies with AI can improve efficiency, usability, and user engagement in travel planning applications. Overall, the developed solution provides a scalable and user-friendly approach, and it has the potential to be further expanded into a more advanced and real-world travel management system.

[1]     MongoDB Inc., “MongoDB Documentation,”  Available: https://www.mongodb.com/docs [2]     React Team, “React: A JavaScript Library for Building User Interfaces,” Available: https://react.dev [3]     Node.js Foundation, “Node.js Documentation,” Available: https://nodejs.org [4]     Express.js, “Express: Fast, Unopinionated, Minimalist Web Framework for Node.js,” Available: https://expressjs.com. [5]     OpenAI, “OpenAI API and GPT Models Documentation,” Available: https://platform.openai.com/docs. [6]     M. Grinberg, Flask Web Development, 2nd ed., O’Reilly Media, 2018. [7]     R. Raj, “Web Applications Using the MERN Stack,” IEEE Internet Computing, vol. 24, no. 3, pp. 78–85, 2020. [8]      S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 4th ed., Pearson, 2021. [9]     J. M. Hellerstein, M. Stonebraker, and J. Hamilton, “Architecture of Modern Web Applications,” Communications of the ACM, vol. 62, no. 6, pp. 44–53, 2019. [10]   Google Developers, “Web Performance Optimization Techniques,” Available: https://developers.google.com.