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

Abstract

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 multi-source 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.

Introduction

Digital transformation has reshaped tourism, with travelers increasingly relying on mobile and online platforms for planning, booking, and exploring destinations. Despite numerous travel apps, challenges like fragmented information, unclear pricing, and limited contextual guidance persist. Pocket Safar addresses these gaps by offering a unified, AI-driven travel platform that integrates bookings, dynamic itinerary planning, personalized recommendations, real-time geolocation, safety services, and community features into a single ecosystem.

The platform leverages machine learning and algorithmic methods such as hybrid recommender systems, clustering, optimization, and geospatial analytics to provide personalized suggestions, route planning, budget management, and group travel matching. Real-time geolocation and safety features identify nearby essential services, while AI-driven itinerary generation optimizes travel paths, timing, and costs. A user-centered design ensures seamless integration of data aggregation, recommendation, optimization, geolocation, and interface modules, creating a comprehensive, intelligent, and context-aware travel management system that enhances convenience, safety, and social engagement for modern travelers.

Conclusion

The development of Pocket Safar illustrates how AI-driven technologies can meaningfully enhance modern travel by unifying personalization, real-time assistance, and intelligent automation within one cohesive platform. Traditional travel systems often lack integrated features and contextual awareness, pushing users to depend on multiple applications for planning, booking, and safety-related tasks. Pocket Safar addresses these issues through advanced recommendation models, dynamic itinerary generation, multi-service price comparison, and geolocation-based identification of essential services. The platform also strengthens user engagement through community-focused travel matching and interactive navigation tools, supporting both solo travelers and groups. By integrating machine learning, spatial analytics, and responsive web technologies, Pocket Safar delivers a seamless, informed, and secure travel experience. Overall, this study demonstrates the strong potential of unified digital ecosystems to positively influence and reshape the future of tourism.

References

[1] Angelo Rossini. Rise of digital has severely disrupted the industry. Retrieved from http://www.EuromonitorInternational.com. Vol 2, no 12, pp 474-478, December 2021 [2] Halawani, Yahya & Soh, Patrick & Halawani, Firas. (2022). Use and Impact of Online Travel Reviews for Planning Free and Easy Holidays. International Journal of Information Systems in the Service Sector. 14. 1-16. 10.4018/IJISSS.287583. Volume 14 • Issue 1 [3] Lan Pan, Ai-Qing Ruan And Wei Zhang. User Adoption Intention Of The Tourism App Based On Experimental Method, Volume:03/Issue:03/March-2021, Impact Factor- 5.354 www.irjmets.com. [4] Yosra Jarrara , Ayodeji Olalekan Awobamiseb and Pedro Sigaud Sellosa. Technological Readiness Index (TRI) and the intention to use smartphone apps for tourism: A focus on in Dubai mobile tourism app, Volume:03/Issue:03/March-2021, Impact Factor- 5.354 [5] A, Rakshith & V, Samhitha & Aravind, Abhirami & Mahto, Sanjay & Manjamma, Chandrappa & Bera, Arijit & Jaison, Feon. (2023). Android Tourist Guide. IJARCCE. 12. 10.17148/IJARCCE.2023.12412. [6] J. Bulchand-Gidumal, E. W. Secin, P. O’Connor, and D. Buhalis, “Artificial intelligence’s impact on hospitality and tourism marketing: exploring key themes and addressing challenges,” Current Issues in Tourism, pp. 1–18, Jun. 2023, doi: https://doi.org/10.1080/13683500.2023.2229480. [7] “Tourism & Sustainable Development Goals – Tourism for SDGs.” https://tourism4sdgs.org/tourism-for-sdgs/tourism-and-sdgs/ , Vol 3, no 4. [8] “Tourism and the Sustainable Development Goals – Journey to 2030, Highlights,” Dec. 2017, doi: https://doi.org/10.18111/9789284419340.Vol 3, no 4, pp 2219-2222, April 2022. [9] UNWTO, “International Tourist Numbers Could Fall 60-80% in 2020, UNWTO Reports | UNWTO,” www.unwto.org, May 07, 2020. https://www.unwto.org/news/covid-19-international-tourist-numbers-could-fall-60-80-in-2020 [10] Wikipedia, “Wikipedia,” Wikipedia.org, Jan. 15, 2001. https://www.wikipedia.org/ [11] G. Adomavicius and A. Tuzhilin, “Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions,” IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 6, pp. 734–749, Jun. 2005, doi: https://doi.org/10.1109/tkde.2005.99. [12] J. B. MacQueen, “Some Methods for Classification and Analysis of Multivariate Observations” ? https://www.eecs.uottawa.ca/~sloyka/elg6108/clustering.pdf [13] L. Breiman, “Random Forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001. [Online]. Available: https://www.stat.berkeley.edu/~breiman/random-forests.pdf [14] G. Adomavicius and A. Tuzhilin, “Toward the Next Generation of Recommender Systems,” IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 6, pp. 734–749, 2005. [Online]. Available: https://pages.stern.nyu.edu/~atuzhili/pdf/TKDE-Paper-as-Printed.pdf [15] L. Breiman, “Random Forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001. [Online]. Available: https://www.stat.berkeley.edu/~breiman/random-forests.pdf [16] Google, Google Maps API Documentation, 2023. [Online].Available:https://developers.google.com/maps [17] Amadeus, Amadeus Travel APIs Documentation, 2024. [Online]. Available: https://developers.amadeus.com.

Copyright

Copyright © 2025 Ambasana Prashant, Amrutha M Doddamani, Allen Palathinkal Kurian, Anish Sutradhar. This 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.