International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 12 | Dec 2025
p-ISSN: 2395-0072
www.irjet.net
AN INTEGRATED SMART TRAVEL PLANNING SYSTEM USING FULLSTACK WEB TECHNOLOGIES 1Ravi Kumar, 2Rishav Yadav, 3Pritesh Chauhan, 4Satyam Narayan Prasad
Student, Dept. of Computer Science & Engineering, KCC Institute of Technology, Greater Noida, Uttar Pradesh, India ----------------------------------------------------------------------------***----------------------------------------------------------------------------applications to compare prices, coordinate schedules, and Abstract - Planning a trip can be a complicated job because develop cohesive travel itineraries. The lack of integration there is a lot of information in different parts of the process. not only increases the time investment required for travel For example, there is transport, hotel stays, tourist activities, planning but also elevates the risk of errors, double funding limitations, etc. In these days when more and more bookings, and missed opportunities for cost optimization. people are travelling with the application of technology, people also require better aids for the planning of their trips. The emergence of smart tourism has introduced new Most travelling processes are modelled on separate bases on paradigms for addressing these challenges through the the dividing between their respective interests. Thus, much application of advanced technologies. Smart tourism systems searching has to be done and many sites covered by the user. leverage big data analytics, artificial intelligence, and realWANDER BHARAT is the result of this research. It is a full web time information processing to enhance the tourist application that transfers traveling questions into a single experience and streamline travel planning processes [4]. body, gives intelligent suggestions based on the interests of the Research has demonstrated that web-based individual with regard to travel and the limitations of funds recommendation systems utilizing multiagent technology upon it, and produces a complete and individual itinerary [1], can significantly improve the efficiency of tourism planning [8]. Through the use of various APIs between many different by providing personalized suggestions based on user subjects, some of which are intelligent recommendations and a preferences and behavioral patterns [1]. Machine learning plan for spending the funds made available, it is possible to algorithms have proven particularly effective in tourism carry out the planning of a trip in one place [6]. The applications, with studies showing that techniques such as application has also been produced through the use of ReactJS Naïve Bayes interest data mining can optimize tour route in the front end and Java Spring Boot in the back end with the planning by analyzing user preferences and historical travel use of a MySQL background for the storing of the data. The data [2]. whole result is that intelligent and personal lines and a more accurate means of better planning of the trip for the user can The integration of deep learning with Internet of Things be obtained through the step. (IoT) technologies has further enhanced the capability of tourism systems to provide real-time, context-aware Key Words: Smart Travel Planner, Full-Stack Web recommendations for tourist attractions in smart cities [3]. Application, Personalized Itinerary, Budget Cloud-based IoT platforms powered Optimization, API Integration. -machine learning algorithms have shown the ability to provide intelligent tourism information scalable and responsive to real-time conditions [6]. These advancements in technology have created conducive grounds for further development sophisticated travel planning tools designed to be able to automatically adjust to changing circumstances, user preferences and other environmental factors like weather patterns, local happenings, and seasonal changes. Recent trends in sustainable tourism involve highlighted the need for incorporating incorporation of concerns about the environment when planning journeys systems. It has been found to be beneficial to model sustainable human systems city trips by integrating carbon emissions data, destination popularity, and seasonal factors into account. Tourism is becoming recommendation algorithms can facilitate more practices of environmentally responsible tourism. [7]
1. INTRODUCTION Travel planning has evolved into an increasingly complex activity that requires careful consideration of multiple interconnected factors, including transportation arrangements, accommodation reservations, activity selection, and budget management. As technology continues to advance and global tourism expands, there is a growing demand for sophisticated systems that can consolidate diverse travel-related information into unified, user-friendly interfaces [5]. However, the current digital landscape presents significant challenges in achieving these objectives. Contemporary travel platforms such as TripAdvisor and Expedia have made substantial contributions to digital tourism by offering specialized services for hotel bookings, flight reservations, and attraction reviews. Nevertheless, these platforms predominantly focus on isolated services rather than providing comprehensive, integrated solutions [5]. This fragmentation creates considerable difficulties for travelers who must navigate multiple websites and
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More advanced algorithms for recommendation have been developed based on cellular geospatial clustering, multivariate weighted collaborative filtering have demonstrated the ability "to balance multiple objectives,
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