International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 04 | Apr 2025
p-ISSN: 2395-0072
www.irjet.net
Intelligent Travel Planning Using AI and NLP: Enhancing Tourist Experiences through Smart Automation Aarti Anthwal 1, Taniya Kataria 2, Riya Bisht 3, Dr. Hridesh Sharma 4 1 B.Tech Student, Dept. of Information Technology, Dronacharya Group of Institution, Greater Noida, India
2 B.Tech Student, Dept. of Information Technology, Dronacharya Group of Institution, Greater Noida, India 3 B.Tech Student, Dept. of Information Technology, Dronacharya Group of Institution, Greater Noida, India
4 Professor, Dept. of Computer Science & Information Technology, Dronacharya Group of Institution, Greater
Noida, India -------------------------------------------------------------------------***-----------------------------------------------------------------------
ABSTRACT- Travel planning is often a demanding and
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complex process due to the abundance of scattered information available on different platforms, which requires travellers to manually search for accommodations, attractions, and logistical details. This study presents an AI-driven travel assistant designed to facilitate and enhance the planning experience by means of automation, personalization, and real-time adaptability. By employing Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP), the system delivers intelligent recommendations customized to user preferences, past behaviour, and realtime information such as weather conditions, crowd levels, and local events. Key features include AI-generated suggestions for destinations and activities, automated itinerary creation, dynamic updates, and budget-conscious accommodation planning. The assistant also uses sentiment analysis to verify reviews and predictive analytics to optimize travel budgets. Unlike conventional planning techniques, which rely heavily on fixed guides and manual decision-making processes, this solution significantly reduces planning time—by as much as 70%—and enhances sightseeing efficiency by 30%. The research evaluates the system against traditional methods, demonstrating a significant improvement in user satisfaction and travel experiences. By utilizing models such as GPT-4 and Google Gemini, the paper contributes to the growing field of smart tourism and demonstrates how AI can revolutionize the future of travel through intelligent, adaptable, and highly personalized solutions.
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Case Study: AI-Optimized Travel Planning in Smart Tourism AI-assisted travel planning has shown an 85% improvement in user satisfaction over manual methods.
1.1.
Challenges
Generate personalized itineraries that align with user preferences. This degree of customization boosts user engagement and trust in AI7.
Impact Factor value: 8.315
Traditional
Travel
Description
Impact Of Challenges
AI, ML, and NLP offer innovative solutions to traditional travel challenges. AI-driven assistants can:
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in
Table 1.1: Key challenges and their impact
1. INTRODUCTION
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Challenges Planning
Even with the abundance of online information, traditional travel planning encounters notable bottlenecks that impede efficiency and personalization.
Keywords: AI Travel Planner, Smart Tourism, Itinerary Optimization, Machine Learning, Personalized Recommendations, NLP
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Optimize travel routes, reducing unnecessary travel time by up to 30%. Adjust plans immediately using up-to-date information (e.g. weather, availability). Collaborate with booking platforms for seamless planning. Analyze reviews via sentiment analysis, improving recommendations by 40%.
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Overabun dance of Informatio n
Excessive information from various sources
Results in decision fatigue and less than ideal travel selection [1]
Absence of Personaliz ation
Standard itineraries fails to represent individual interests
Travellers fins it difficult to discover customized experiences [2].
TimeIntensive Procedure s
Manually investigating, evaluating, organizing
Takes 5–10 hours per journey, heightens stress [3].
Inefficient Routes
Inadequately scheduled itineraries with prolonged travel duration
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Lowers overall trip effectiveness by 30% [4].
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