Pocket Safar: An AI-Driven Smart Travel Recommendation and Dynamic Itinerary Planning System

The rapid growth of digital travel platforms has streamlined booking processes, many existing systems still struggle to

deliver deep personalization, effective budget planning, contextual safety support, and meaningful community-based features. To

overcome these limitations, our team developed Pocket Safar, an AI-driven travel management platform that integrates multisource price comparison, dynamic itinerary generation, emergency-essential detection, and social travel collaboration into one

cohesive system. The platform enables users to book flights, hotels, trains, cabs, and cruises while applying hybrid

recommendation techniques such as content-based filtering, collaborative filtering, matrix factorization, clustering models, and

advanced geolocation analytics.Pocket Safar also strengthens budget planning by incorporating dynamic programming, linear

and integer programming, and genetic algorithms to create cost-efficient and personalized travel plans based on user budgets,

dates, and preferences. Route optimization methods including TSP, A*, and Dijkstra’s algorithm, supported by spatial indexing,

improve navigation accuracy and assist in locating essential nearby services. NLP models enhance traveler matching and

community engagement, while predictive modeling assesses price trends and travel behavior.Initial results show that Pocket

Safar reduces manual planning effort, increases itinerary relevance, enhances safety awareness, and improves engagement for

both solo and group travelers, offering a more personalized and efficient travel experience.