Abstract
Aimed at current problems in tour route planning, this research proposes a tour route planning algorithm based on the data mining of tourist sights based on their precise interest. It further studies on the aspect of tourist interests and needs, tourist sight feature attributes, data mining of tourist sights based on their precise interest and optimal tour route planning, and then designs and performs experiment to testify the feasibility and superiority of the algorithm. First, tourist interest and need label matrix and tourist sight feature attribute matrix are set up. Tourist sight attribute factor is formed by mining text encyclopedic data, and combining with other factors, tourist sight clustering algorithm is set up to output tourist sight clusters. Second, data mining algorithm of tourist sights based on their precise interest is set up, which outputs the optimal tourist sights not only match the tourists' interests and needs but also optimally distribute around to produce the lowest travel costs. Third, based on precise interested tourist sights, combining with geographic information data, traffic information data and tourist sight information data, optimal tour route planning algorithm based on interest label is set up. It outputs maximum heap and complete binary tree with descending order interval motive values and corresponding edge clipping circle, which directly reflects the satisfaction content of each tour route on tourists. Tourist sight domain and geographic spatial data of Zhengzhou city are collected as basic data to design the experiment, and it testifies the algorithm's feasibility, effectiveness and practicalness. Meanwhile, the experiment brings in three commonly used route planning algorithms as control group to make comparison with the developed algorithm on the aspect of optimal route, motive value, motive value difference, algorithm performance and guide map,etc., which testifies that the developed algorithm has superiority to traditional algorithms. The research indicates that this developed algorithm solves the problems on tour route planning and it is an effective, feasible and practical algorithm.
Highlights
Tour route planning is one of the most important research contents of smart tourism
The research lays emphasis on tourists’ interests and needs, tourist sight feature attributes, data mining of tourist sights based on their precise interest and optimal tour route planning, and it aims to meet the satisfaction of tourists
The distribution of the tourist sights meets the four basic conditions preset in the experiment, and they could be the basic data source for the experiment
Summary
Tour route planning is one of the most important research contents of smart tourism. Setting up smart tourist sight and tour route recommender system is the indispensable component of the smart tourism construction, and its purpose is to provide recommendation and decision support for tourists, especially. Aimed at the problem of insufficiency study on typical tourist sights in tourism city, this research tends to set up the clustering objective function σ (cr , c¬r ) based on the parameter factors of text encyclopedia data mining, attraction index, optimal visiting time and tour expense, etc. The research lays emphasis on tourists’ interests and needs, tourist sight feature attributes, data mining of tourist sights based on their precise interest and optimal tour route planning, and it aims to meet the satisfaction of tourists. This section aims to study on tourists’ interests and needs as well as city tourist sights’ feature attributes, based on which, tourist sight clustering algorithm is designed and set up This is the precondition for the data mining of tourist sights based on their precise interest. This section concludes the whole research work, research method and experimental results, concludes the future work
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