International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Tripease – Smart Travel Companion for Tourist |
|---|---|
| ABSTRACT | Travel planning is a complex and fragmented task, often requiring users to navigate multiple platforms for information on attractions, accommodations, and logistics. Manual research and itinerary coordination are time-consuming and frequently lead to sub-optimal travel experiences. As the demand for personalized travel continues to increase, there is a growing need for automated systems that can efficiently convert user requirements into structured, actionable travel plans, especially since traditional search engines fail to provide integrated reasoning and present overwhelming amounts of disconnected data. With advancements in Artificial Intelligence, particularly in Large Language Models (LLMs) and Vector Databases, we developed an intelligent companion to automate this orchestration process. The system utilizes advanced local models like Llama 3.2 and Retrieval-Augmented Generation (RAG) through ChromaDB for factual grounding and context retrieval, maintaining high response relevance. Key challenges addressed include handling diverse user intents, mitigating model hallucinations, accounting for geospatial distances using the Haversine formula, and optimizing performance for limited local hardware resources. By operating entirely offline to ensure complete data privacy and security, the system provides a cost-effective, highly responsive solution capable of generating structured, multi-day itineraries in near real-time. |
| AUTHOR | Y. SUNAINA, B. REVATHI,A. VAMSI, V. PAVAN SRI RAM, A. UTHPALA DEVI, R. SAI DEEPTHI, M. ROHITH NAIDU Assistant Professor, Department of CSE (Data Science), NSRIT, Vishakhapatnam, India Student, Department of CSE (Data Science), NSRIT, Vishakhapatnam, India |
| VOLUME | 182 |
| DOI | DOI: 10.15680/IJIRCCE. 2026.1403056 |
| pdf/56_Tripease – Smart Travel Companion for Tourist.pdf | |
| KEYWORDS | |
| References | 1. Meta AI (2024). "The Llama 3 Herd of Models." Technical report for Llama 3.2. Available at: https://arxiv.org/abs/2407.21783 2. Reimers, N. & Gurevych, I. (2019). "Sentence-BERT." Foundation for semantic search and all-MiniLM embeddings. Available at: https://arxiv.org/abs/1908.10084 3. "Retrieval-Augmented Generation (RAG) for Personalized Travel Itinerary Planning" (IJSR, 2025). Domain research on travel AI. Available at: https://ijsret.com 4. "Privacy by Design: The Benefits of Local LLMs in Sensitive Applications." Justification for local execution. Available at: https://unite.ai 5. ChromaDB Technical Documentation. Guide for high-dimensional vector search. Available at: https://docs.trychroma.com/ 6. FastAPI & Streamlit Official Framework Documentation. Core technology stack guides. Available at: https://fastapi.tiangolo.com/ and https://docs.streamlit.io/ 7. "Chatbot Using a Knowledge in Database" (Setiaji Bayu, 7th ISMS). Foundation for retrieval logic. Available at: https://ieeexplore.ieee.org/document/7546416 8. "Developing a Chatbot using Machine Learning" (K. Jwala, IJRTE). Approach to building conversational systems. Available at: https://www.ijrte.org/ 9. Haversine Formula for Geospatial Optimization. Technical logic for distance-based ranking. Available at: https://en.wikipedia.org/wiki/Haversine_formula 10. OpenWeatherMap & External API Performance Guides. Integration for real-time climatic context. Available at: https://openweathermap.org/api |
