Personalized Route Planning System For Tourism: Models, Algorithms, And Software Tools

The paper presents a personalised route planning system to individual tourism in Uzbekistan with emphasis on the rich cultural and natural attractions of the nation. Through combinations of Geographic Information Systems (GIS), hybridized recommendation systems, and sophisticated optimization algorithms, the system is able to deal with the issue of design of tailored travel itineraries. The Multi-Objective Variable Neighborhood Search (MOVNS) and Hybrid Personalized Search Genetic (HPSG) algorithms are the proposed methods that attempt to optimize the routes according to the travel time, cost, and user satisfaction using real-time information (e.g., weather, traffic). The software application has interactive maps in a GIS format, scalable PostgreSQL/PostGIS database, and an easy to use web interface. On a dataset of 100 users and 50 destinations within Uzbekistan, it is shown that the system cuts travel time by 15 percent, cost by 12 percent as well as results in 90 percent user satisfaction compared to traditional algorithms, such as Dijkstra's and A. This improves intelligent tourism systems through the integration of data science, GIS, and AI, and is applied to emerging markets such as Uzbekistan. The next research is going to be based on the real-time feedback integration and sustainability measures.

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