Paper Details

Name: Disha Sharma, Usman Ali, Aman Kumar Aditya, Gurleen Kaur & Astha Rathore
Country: India

Year: 2026
Volume: Volume No: 13, January, Year: 2026 (Special Issue)
Page Number: 45-51
DOI: https://doi.org/10.5281/zenodo.18934961

ABSTRACT
Weak environmental factors, such as weather conditions, shifting user preferences, along with road traffic control impact travel efficiency in tourism. Therefore, real-time route optimization models are needed for ensuring smooth and efficient travel for the user. The paper explore how deep learning methods can boost route optimization in tourism systems. The application uses real-time position system and traffic report and weather forecast data to adjust travel routes which delivers customized and optimized routes. Travelers obtain adaptable route recommendations from the system after it factors in their preferences and past travel data and external boundary restrictions to enhance their whole travel experience. If we compare the traditional models over the models used in this paper that is Deep learning-models then we could clearly see a better flexibility and higher accuracy as well as increased computational efficiency. The integration of deep learning
technology improves real-time decision processes in tourism-based navigation systems which leads to time reduction and increases user satisfaction levels.

General Terms
Deep Learning, Route Optimization, Tourism Navigation, Real-time Systems, Traffic Management, Weather Forecasting, User Preferences, Computational Efficiency

Keywords: Deep learning, route optimization, tourism, real time navigation, traffic prediction, personalized travel