Improved On-Demand Travel Route Planning Model with Interest Fields

Abstract

Intelligent tourism route planning is an important element of smart tourism, and the current tourism route planning has problems such as strong subjectivity and low personalization considering tourists’ interests. To solve the problems of current tourism route planning, an improved interest field travel route planning model is proposed. Firstly, an intelligent interest field extraction model is established. Secondly, an improved greedy algorithm is designed to reduce the risk of missing the optimal solution, strengthen the local search capability, and improve the solution accuracy of the algorithm. The extracted routes of interest sites are planned, and a motivated iterative value output model is established. The experimental results demonstrate that the selected routes are shorter and less expensive than the traditional model. By iterating the actual data to obtain the iterative values of different tourist route motivations and the sequential guide map of attractions based on tourist interests, the optimal and suboptimal routes that satisfy the tourist motivation interests are analyzed. This model has strong feasibility and practical significance for smart tourism route planning.

Optimizing Ecotourism in North Taihu Lake, Wuxi City, China: Integrating Back Propagation Neural Networks and Ant-Colony Algorithm for Sustainable Route Planning

“…Xie (2018) emphasized the importance of scientifically and rationally designed tourist routes to improve tourist satisfaction and promote the longterm development of tourist destinations, particularly in rural ecotourism. Yan (2022) proposed an improved interest field travel route planning model to enhance the personalization of tourism route planning by considering tourists' interests and using an intelligent interest field extraction model. These studies highlight the significance of route planning in ecotourism and provide valuable insights into optimizing tourist routes based on various factors and preferences.…”

Combine deep learning and LKH algorithm to solve multiconstraint tourism planning

“…The brute force search method can be very inefficient and take a long time to calculate when the search space is very large or the number of route combinations is large. Brute force search is a basic search method in travel route planning, but it is usually used in small-scale problems or as part of optimization algorithms to help find the best route [6] .…”