Shobana, M. and R., Sairam and M., Vishalini K. and S. A., Yogeshwaran and R., Poorna Vignesh (2025) Personalized Travel Itenirary Planning. International Journal of Innovative Science and Research Technology, 10 (4): 25apr1606. pp. 2722-2732. ISSN 2456-2165
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Abstract
The AI-driven system prioritizes customization by analyzing user preferences, real time traffic data, public transport schedules, and weather conditions to generate itineraries tailored to specific needs. Unlike conventional platforms, this solution dynamically adapts to changing circumstances, ensuring that users receive recommendations optimized for cost, time, and convenience. For instance, a traveler seeking a budget-friendly route will be offered alternatives that minimize expenses without sacrificing efficiency. In contrast, a commuter prioritizing punctuality will receive updates aligned with their schedule. Travelers are given the ability to confidently make decisions based on information thanks to these intelligent adjustments and the user-friendly interface. Central to the system's success is its seamless integration of key modules. A robust user input mechanism simplifies data collection, allowing travelers to specify preferences such as destination, travel time, and preferred transportation modes. The AI processes these inputs to generate highly accurate and adaptive travel plans, while a visually appealing interface displays recommendations through interactive maps and route highlights. Users can rely on the platform to handle all logistical details, saving time and stress when planning complex multimodal trips or navigating a single route. The Smart AI Travel Itinerary Planner further distinguishes itself through real time adaptability. The system ensures that users are kept informed of potential disruptions by utilizing APIs for live traffic updates, availability of public transportation, and weather forecasts. It provides alternative routes and options to guarantee smooth travel experiences, even under unexpected circumstances. Additionally, the platform continuously learns from user interactions, refining its recommendations to align better with evolving preferences and behaviors. **With its focus on user-centric design, the system bridges the gap between advanced technology and real-world usability, creating an experience that is both efficient and intuitive. Predictive analytics will be incorporated into future updates to provide proactive solutions, further improving the travel planning process.
Item Type: | Article |
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Subjects: | T Technology > T Technology (General) |
Divisions: | Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science |
Depositing User: | Editor IJISRT Publication |
Date Deposited: | 09 May 2025 10:57 |
Last Modified: | 09 May 2025 10:57 |
URI: | https://eprint.ijisrt.org/id/eprint/786 |