Abstract: Trip planning often involves using multiple platforms for destination research, map navigation, and manual itinerary organization, leading to fragmented and inefficient planning. Existing tools typically offer either basic AI-generated suggestions or isolated map-based exploration, lacking real-world feasibility and transparency. This paper presents Wanderly AI, an enhanced AI-powered personal travel assistant that integrates map-aware large language model (LLM) reasoning with full-stack web technologies. The system generates practical itineraries by incorporating geographic constraints such as distance, travel time, coordinates, and map context. To ensure consistency and reliability, reproducibility mechanisms using seeded prompts and version-controlled templates are implemented. Additionally, explainable AI techniques are used to justify itinerary decisions, including activity selection, sequencing, and timing based on user preferences and feasibility. The proposed system delivers realistic, personalized, and transparent travel planning, addressing key limitations of conventional trip-planning applications.
Keywords: Travel planning, Map-aware AI, Itinerary generation, Explainable AI, Reproducible AI, Geographic constraints
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DOI:
10.17148/IJARCCE.2026.15216
[1] Dnyaneshwar Gunjal, Sakshi Shirapure, Devyani Vizekar, Prathmesh Sonar, "WanderlyAI – AI Powered Personal Travel Assistant for Destination Planning and Experience Optimization," International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2026.15216
