Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{193522,
        author = {Suganth A and Subasri P and Dharshiga B and Vikasini A L and Aparna C},
        title = {An AI-Driven Travel Blogging Platform with Location-Aware Break-Stop Tourist Discovery and Real-Time Navigation},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {1205-1208},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=193522},
        abstract = {Travel blogging platforms have become increasingly popular as travelers rely on digital media to plan, document, and share their experiences. However, most existing blogging systems operate as static content-sharing platforms without real-time contextual intelligence during active journeys. Travelers frequently take short break-stops during long-distance travel for rest, refreshments, or fuel, yet they often remain unaware of nearby tourist attractions located close to their stopping points. Conventional navigation applications primarily focus on route optimization and general points of interest; while blogging platforms function independently without integration into live travel behavior. This separation limits exploration opportunities and reduces the practical usability of travel content during real-time journeys. This paper presents an AI-driven location-aware travel blogging system that integrates real-time break-stop detection, tourist discovery, and navigation assistance within a unified platform. The proposed system detects the user’s geographical location through GPS services or manual map marking and dynamically retrieves nearby tourist attractions within a defined radius. An intelligent recommendation mechanism filters attractions based on relevance, proximity, and user interests. The system further links discovered locations with user-generated travel blogs to provide contextual insights as per the user prefernces.},
        keywords = {Travel Blogging System, Location-Aware Computing, Tourist Discovery, Artificial Intelligence, Real-Time Navigation, Smart Tourism Applications.},
        month = {March},
        }