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AI-Based Personalized Travel Planning and Recommendation System: A Unified Framework for Intelligent Trip Planning
Subject area: Science,Engineering and Technology · Area of research: AI-Based Travel Recommendation Systems
Abstract
A growing number of travel planning activities involve managing multiple pieces of often competing data about destinations, budget, weather, attractions' availability, transportation options and personal preferences that are typically distributed across a number of isolated online services. Traditional travel-planning tools tackle these issues one at a time: choosing or navigating, and rarely include personalization, predictive analytics, and multi-constraint reasoning within a single interface. We propose a unified framework for personalized travel planning that integrates personal preferences, context-based destination recommendation, budget- and weather- aware filtering, and route and distance analysis, and AI-assisted itinerary generation. The framework is inspired, and to some extent generalized, from the design of the ongoing academic research prototype TripCraft, an AI-powered agent for trip planning. We build upon the state of the art of recommender-systems theory, context-aware recommendation, tourist trip design (route optimization) literature, and recent large language model-based agents for travel-planning to develop a system-level description, a hybrid AI/ML approach, and a step-by-step algorithmic process for creating personalized itineraries that take preference, budget, weather and route constraints into account. We provide preliminary proof of concept based on a synthetic evaluation on fabricated records using baseline comparisons and constraint-compliance metrics. The findings are only indicative of the possible effectiveness, but do not constitute proof of concept. The expected contribution is the conceptual and methodological foundation for unifying preference-, budget-, weather- and route awareness in intelligent travel planning.
Keywords
artificial intelligence; personalized travel planning; machine learning; recommender systems; itinerary generation; TravelTech
References
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How to cite this paper
@article{1723908,
author = {Shubham Pandey, Satyam Anand, Sujal Das, Raushan Kumar Tiwari},
title = {AI-Based Personalized Travel Planning and Recommendation System: A Unified Framework for Intelligent Trip Planning},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {4},
pages = {1721-1732},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1723908.pdf},
abstract = {A growing number of travel planning activities involve managing multiple pieces of often competing data about destinations, budget, weather, attractions' availability, transportation options and personal preferences that are typically distributed across a number of isolated online services. Traditional travel-planning tools tackle these issues one at a time: choosing or navigating, and rarely include personalization, predictive analytics, and multi-constraint reasoning within a single interface. We propose a unified framework for personalized travel planning that integrates personal preferences, context-based destination recommendation, budget- and weather- aware filtering, and route and distance analysis, and AI-assisted itinerary generation. The framework is inspired, and to some extent generalized, from the design of the ongoing academic research prototype TripCraft, an AI-powered agent for trip planning. We build upon the state of the art of recommender-systems theory, context-aware recommendation, tourist trip design (route optimization) literature, and recent large language model-based agents for travel-planning to develop a system-level description, a hybrid AI/ML approach, and a step-by-step algorithmic process for creating personalized itineraries that take preference, budget, weather and route constraints into account. We provide preliminary proof of concept based on a synthetic evaluation on fabricated records using baseline comparisons and constraint-compliance metrics. The findings are only indicative of the possible effectiveness, but do not constitute proof of concept. The expected contribution is the conceptual and methodological foundation for unifying preference-, budget-, weather- and route awareness in intelligent travel planning.},
keywords = {artificial intelligence; personalized travel planning; machine learning; recommender systems; itinerary generation; TravelTech},
month = {October},
}