Smart travel planning system based on kernel density estimation and similarity metric clustering algorithm

Growing economy has boomed tourism, but intelligent travel planning services restrict long-term and stable tourism development. Typically, travel planning requires substantial time and cost. And currently, less focus on user preferences in most tourist attraction recommendations also results in low efficiency. In this paper, firstly, the K-means algorithm is introduced for clustering analysis of user behavior or interests, so as to better understand user preferences. Gaussian kernel density estimation and similarity measurement are also adopted to improve the traditional K-means algorithm, which provides the foundation for a tourist attraction recommendation model. Then, to further improve transportation route planning, the study introduces the ant colony algorithm, adaptive crossover strategy and local search algorithm to enhance the traditional genetic algorithm for an optimized travel path planning model. The outcomes show that the improved clustering algorithm possesses the highest accuracy of 0.96 and 0.78 in Iris and Glass datasets respectively, along with a sum of squared errors of 96.73 and 476.48 respectively. The shortest running time in the Yeast data-set is 1.22 s. The improved clustering algorithm with 50 nearest neighbors has an average absolute error value of 0.749, and its longest running time does not exceed 1 s. In summary, the model developed in this study is highly applicable to personalized recommendation services and efficient travel routes.