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@article{194725,
        author = {Udhaya Prakash .M},
        title = {TRIPTALK: AN AI-DRIVEN CHATBOT FOR SMART TRAVEL PLANNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {6946-6954},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194725},
        abstract = {Existing travel and railway service platforms are highly fragmented and rely on rigid, form-based interfaces that require users to navigate multiple applications for ticket booking, payments, food ordering, accommodation search, and tourist planning. This lack of integration increases booking time, causes user confusion, and limits real-time assistance during travel. Manual data entry, disconnected payment flows, and minimal personalization further reduce system efficiency and negatively impact the overall user experience. To address these challenges, this project proposes TripTalk: A Chartered Al Agent for Smart Travel Planning, an integrated chatbot-based web platform that delivers end-to-end travel services through natural language interaction. The system employs a Large Language Model (Gemini Al) as the core conversational intelligence, supported by Natural Language Processing (NLP) techniques such as intent classification, entity extraction, and contextual dialogue management. A retrieval-augmented generation (RAG) approach is used to ground responses with real-time data obtained through Rapid APIs, enabling accurate retrieval of train schedules, seat availability, and fare details. The Al agent applies decision-support intelligence and recommendation algorithms to suggest optimal train options based on user preferences and constraints. Secure UPI-based digital payment integration enables seamless transactions, followed by automatic generation of downloadable e-tickets. Beyond ticket booking, the Al agent supports in-travel food ordering through context awareness, performs Al-driven itinerary planning. acting as a recommendation system based on destination and duration of stay, and provides budget-based hotel and restaurant suggestions using Al-based filtering and ranking mechanisms. The system is implemented using HTML, CSS, and JavaScript for the frontend, with Python (Flask API) for backend services. The proposed solution reduces manual intervention, enhances usability, and offers a scalable, intelligent approach to modern},
        keywords = {Artificial Intelligence, Travel Chatbot, Natural Language Processing, Smart Travel Planning, Recommendation System.},
        month = {March},
        }