ExploreEasy: Smart and all-in-one trip management application - Yap, Pei Nee (2025)
As travellers increasingly seek tailored and efficient experiences, current travel applications often fail to address diverse requirements and adapt to real-time changes. This research presents an AI-driven trip planning and recommendation system that employs a Hybrid Recommendation Algorithm, integrating Collaborative Filtering (CF) and Content-Based Filtering (CBF) to provide highly customised travel itineraries. Core components include automated accommodation suggestions, an inflation-aware budget estimation and management module that applies Jaccard Similarity and Weighted Averaging for accurate budget ranges, and a cost split feature for fair expense sharing among travellers. The system also provides real-time budget alerts to enhance financial transparency and control. To improve travel efficiency, the system incorporates intelligent route optimisation using the Travelling Salesman Problem (TSP), ensuring time-efficient and logically sequenced itineraries. Additionally, a similar-place substitution feature leveraging Geographic Filtering and Quality Thresholds increases flexibility by dynamically suggesting contextually relevant alternatives. Furthermore, the integration of real-time and extended weather forecasting enables dynamic itinerary modifications to enhance safety and adaptability. The system’s effectiveness was evaluated with real travel data, revealing significant improvements in personalisation, flexibility, financial confidence, and user satisfaction. By combining a hybrid recommendation engine with innovative features such as weather-aware itinerary adjustments, budget monitoring, expense splitting, and substitution-based adaptability, this project delivers a more intelligent, responsive, and user-centric trip planning experience than conventional platforms.