Paper
11 July 2024 Combine deep learning and LKH algorithm to solve multiconstraint tourism planning
Ling Zhong, Shibo Lin
Author Affiliations +
Abstract
Multi-constraint travel route planning is a challenging problem, which involves multiple constraints such as transportation, time, cost and traveler preference. In recent years, the development of deep learning technology has provided new possibilities to solve this problem. In this paper, a method combining deep learning and LKH algorithm is proposed to solve the multi-constraint tourism planning. First, we collect tourism data, including scenic spot information, transportation information and traveler preferences. Then, we trained the tourism data using deep learning models, such as neural networks, to learn the correlations between attractions and traveler preferences. We designed a multi-task learning model that can simultaneously predict multiple constraints such as travel time, distance, cost, etc. Finally, we apply the LKH algorithm to the output of the deep learning model to obtain the optimal travel route. Experimental results show that our method can efficiently generate travel routes that meet travelers' needs under multiple constraints. This study provides a new solution for multi- constraint travel planning, and also provides a practical application case for the combination of deep learning and traditional algorithms.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Ling Zhong and Shibo Lin "Combine deep learning and LKH algorithm to solve multiconstraint tourism planning", Proc. SPIE 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024), 1321013 (11 July 2024); https://doi.org/10.1117/12.3034942
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KEYWORDS
Deep learning

Education and training

Evolutionary algorithms

Mathematical optimization

Matrices

Genetic algorithms

Data modeling

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