Personalized Itinerary Generation Prototype Using K-Means Clustering and 2-Opt Optimization with Integrated User Content Sharing (in London)

Conventional travel planners often offer one-size-fits-all schedules and fixed tour packages, overlooking individual tastes and the wealth of peer insights. To enhance the personalization and interactivity of modern travel planning tools, this study presents a modular AI-based web application that generates optimized London itineraries tailored to individual preferences. The system integrates three core components. First, an AI-based itinerary generation module applies K-Means clustering—guided by the Elbow Method and Silhouette Analysis—to group Points of Interest (POIs) into thematic clusters that are both geographically compact and interest-aligned. Second, a travel route optimization module uses the 2-Opt algorithm, leveraging Haversine distance calculations to reorder each day’s POIs, reducing intra-day travel distances by 12–18% on average. Third, a user-generated content module, built on Flask and SQLite, enables travelers to share blogs and vlogs and engage through likes and comments, fostering a socially enriched planning environment. The integration of algorithmic clustering and route optimization ensures that daily itineraries are spatially efficient and contextually relevant, while the social features heighten engagement and perceived value. Early user feedback indicates that the community-driven content enhances trust and decision-making quality. This dual-layered architecture bridges data-driven automation with collaborative user input, marking a shift from static itinerary tools to adaptive, socially-informed travel platforms. The proposed system offers a scalable framework for future smart tourism applications that aim to combine personalization, optimization, and participatory design.