Travel Genie: An Intelligent End-to-End Travel and Itinerary Planning System

Travel itinerary planning is a complex optimization problem that requires balancing multiple factors such as budget,

trip duration, accommodation preferences, attraction categories, transportation accessibility and user interests. While existing

travel planning systems can generate personalized itineraries, they often lack a comprehensive optimization framework that

simultaneously considers geographical proximity, user preferences. To address this challenge, this paper presents Travel Genie,

an intelligent travel and itinerary system designed to generate intelligent and personalized travel itineraries for tourists visiting

London. The proposed framework integrates clustering and optimization techniques including K-Means Clustering, Constraint

Satisfaction Problem (CSP), Mixed Integer Linear Programming (MILP) and Genetic Algorithm (GA) to generate feasible and

personalized travel itineraries. User preferences including trip type, budget, group size, trip duration, preferred attraction

categories and accommodation requirements are collected through an interactive interface. K-Means clustering groups

geographically similar Points of Interest (POIs) to reduce travel complexity, while CSP filters infeasible travel options based on

user-defined constraints. MILP then selects an optimal set of attractions that maximizes itinerary quality while satisfying all

constraints. Finally, the Genetic Algorithm enhances itinerary diversity and minimizes unnecessary travel between attraction

clusters. The generated itineraries are organized into daily schedules with recommended accommodations, nearby transportation

information and time-slot allocation for each Point of Interest through a user-friendly Streamlit interface. Experimental

evaluation demonstrates the effectiveness of the proposed framework in generating feasible and personalized itineraries with

improved clustering quality, high constraint satisfaction and efficient itinerary optimization. The proposed approach provides an

effective decision-support system for automated travel planning and enhances the overall travel experience through intelligent

itinerary generation