Travel plan for tourists: minimum access path and route circuit in Jalapão State Park

This article presents the proposal for a model travel plan for tourists in the Jalapão State Park [PEJ - Parque Estadual do Jalapão], located in the State of Tocantins, Brazil. The research shows the use of the Gurobi Optimizer library in Python Software associated with using Miller-Tucker-Zemlin (MTZ) constraints to ensure a viable route circuit. Through the Traveling Salesman Problem (TSP), two viable optimal routes are presented for two research problems: i) minimize the distance of access to the PEJ from the city of Palmas -TO and ii) find an optimal route path for tourists considering some of the most relevant points of the PEJ. The study presents a viable solution to route problems and contributes with an actual model, showing that TSP and the use of restrictions MTZ can be adequate to solve these problems and others to be solved in PEJ.This article presents the proposal for a model travel plan for tourists in the Jalapão State Park [PEJ - Parque Estadual do Jalapão], located in the State of Tocantins, Brazil. The research shows the use of the Gurobi Optimizer library in Python Software associated with using Miller-Tucker-Zemlin (MTZ) constraints to ensure a viable route circuit. Through the Traveling Salesman Problem (TSP), two viable optimal routes are presented for two research problems: i) minimize the distance of access to the PEJ from the city of Palmas -TO and ii) find an optimal route path for tourists considering some of the most relevant points of the PEJ. The study presents a viable solution to route problems and contributes with an actual model, showing that TSP and the use of restrictions MTZ can be adequate to solve these problems and others to be solved in PEJ

Implementing k-means clustering algorithm in collaborative trip advisory and planning system

Fueled by the help of the Internet, more and more tourists nowadays tend to plan trips by themselves in order to have trip plans that meet their preferences and convenience perfectly. However, tourists may face some problems when they plan a trip by themselves, which makes the whole trip planning process challenging and tiring. These problems include extensive tourism information, manually constructing an itinerary for a trip and difficulty in satisfying the needs of all trip participants. Therefore, a web-based trip planning system is proposed in this project to solve all these problems in order to help tourists in planning their desired trips more effectively and efficiently. This system will help users to search for attractions and restaurants in Southeast Asian countries faster by providing filtering and prioritizing features. This system also facilitates the decision-making process of tourists when choosing places to visit and restaurants by utilizing the power of word-of-mouth (reviews). Besides, this system will aid tourists by reducing the need to manually construct the itinerary for a trip. The k-means clustering algorithm will be used to auto-arrange the trip itinerary to ensure places close to each other are arranged to be visited on the same day so that tourists can save on unnecessary transport costs and time. Lastly, this system promotes collaborative trip planning by providing a platform for all the participants in a trip to discuss and plan their trip together

Artificial Intelligence as Catalyst for the Tourism Sector: A Literature Review

The analysis of Artificial Intelligence techniques and models used in the tourism sector provides insightful information for the management and innovation of this industry. In this paper, we conduct a comprehensive review of the different techniques and models, in regards to Artificial Intelligence when applied to the tourism industry. Specifically, we present a categorization of Artificial Intelligence applications used in different areas of tourism. The results allow to recognize valid studies and useful tools for the activation and growth of the tourism sector, an industry that represents a significant increase in the Gross Domestic Product of various economies and supports the development of life conditions for their inhabitants. Artificial Intelligence applications generate more personalized travel experiences, improve the efficiency of tourism services and strengthen the tourism competitiveness of the destination.&nbsp