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Yoheswari S
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Page No: 1 - 12
Abstract : The evolution of artificial intelligence in travel applications has paved the way for intelligent route planning systems that adapt to user preferences and contextual needs. This paper proposes an AI-enabled smart travel route framework that leverages semantic-aware keyword extraction and personalization to generate optimized travel itineraries. The framework integrates natural language processing (NLP) to extract semantic relevance from user queries and historical preferences, aligning them with location-based metadata and user behavior. Machine learning models are employed to rank and recommend routes based on popularity, travel constraints, user intent, and temporal factors. Unlike conventional route planners, the proposed system focuses not only on distance and time but also on user-defined semantic preferences such as scenic value, cultural significance, or activity interest. Through modular components including keyword extraction, context matching, and adaptive learning, the system offers a dynamic and personalized travel experience. Experimental evaluations indicate significant improvements in user satisfaction, relevance of recommendations, and route efficiency compared to traditional systems.
Keyword AI in travel planning, semantic keyword extraction, smart route recommendation, personalization, NLP in tourism, context-aware travel, machine learning in route planning, user-centric itinerary generation
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The digital revolution has significantly reshaped how we plan and experience travel, with route
planning applications being one of the most crucial components of the travel ecosystem. Traditional
route planning systems, such as those employed by Google Maps and other GPS-based applications,
focus primarily on optimizing logistical aspects of travel, such as minimizing distance or time.
While these applications serve their purpose effectively in terms of helping travelers reach their
destinations, they often over look the deeper semantic elements of travel. These elements reflect the
emotional, cultural, and recreational desires that shape how individuals experience their journeys. For
example, a user seeking a “romantic spot for dinner” or a “family-friendly park with less crowd” may
not find suitable results using traditional systems, as they are focused primarily on distance and time,
rather than understanding user preferences or intent. This limitation of conventional route planners
creates a need for more intelligent, context-aware systems that can provide personalized,
meaning full travel suggestions An AI-Enabled Smart Travel.

An AI-Enabled Smart Travel


In recent years, artificial intelligence (AI) and machine learning (ML) techniques have become
increasingly influential in a variety of domains, including travel planning. AI-based systems are
capable of processing large volumes of complex data, including historical behavior, semantic signals,
and real-time contextual information. These capabilities enable the creation of intelligent travel route
frameworks that go beyond the conventional “point A to point B” model. The integration of Natural
Language Processing (NLP) and semantic analysis can help in understanding the nuanced language of
travel queries, allowing AI to infer deeper preferences that may not be immediately obvious.

These systems are more dynamic and responsive to the specific needs of users, offering recommendations
based not only on location but also on factors that influence user experience, such as weather or local
events

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