Abstract
This study designed a tour-route-planning and recommendation algorithm that was based on an improved AGNES spatial clustering and space-time deduction model. First, the improved AGNES tourist attraction spatial clustering algorithm was created. Based on the features and spatial attributes, city tourist attraction clusters were formed, in which the tourist attractions with a high degree of correlation among attributes were gathered into the same cluster. It formed the precondition for searching tourist attractions that would match tourist interests. Using tourist attraction clusters, this study also developed a tourist attraction reachability model that was based on tourist-interest data and geospatial relationships to confirm each tourist attraction’s degree of correlation to tourist interests. A dynamic space-time deduction algorithm that was based on travel time and cost allowances was designed in which the transportation mode, time, and costs were set as the key factors. To verify the proposed algorithm, two control algorithms were chosen and tested against the proposed algorithm. Our results showed that the proposed algorithm had better results for tour-route planning under different transportation modes as compared to the controls. The proposed algorithm not only considered time and cost allowances, but it also considered the shortest traveling distance between tourist attractions. Therefore, the tourist attractions and tour routes that were suggested not only met tourist interests, but they also conformed to the constraint conditions and lowered the overall total costs.
Highlights
Tourists are the core of tourism activity
This study designed and tested a tour-route-recommendation algorithm that was based on an improved agglomerative nesting (AGNES) spatial clustering and space-time deduction model, focusing on precise interest-matching, urban tourist-attraction spatial clustering, spacetime deduction of the traveling process, and precise tour route searching based on the transportation mode
Since the proposed algorithm’s experimental environment conforms to these conditions, the Dijkstra algorithm and the A* algorithm were chosen as controls to plan the travel routes for the sub-unit Φ, and the control group algorithms were defined as Algorithm 1 (A1) and Algorithm 2 (A2)
Summary
Tourists are the core of tourism activity. A key issue of smart tourism is how to improve tourist satisfaction and provide the best experience. This study designed and tested a tour-route-recommendation algorithm that was based on an improved AGNES spatial clustering and space-time deduction model, focusing on precise interest-matching, urban tourist-attraction spatial clustering, spacetime deduction of the traveling process, and precise tour route searching based on the transportation mode. The proposed method is a one–one mode in which tourist interests were studied and set as the specific preconditions to extract certain tourist attractions, and the path-searching algorithm was used to find out the optimal tour route. The studies on the tourist attraction and tour route recommendation are based on the fuzzy recommendation, while the proposed algorithm is under the consideration and constraint of the real-world city tourism environment, road conditions, and transportation modes, it could find out the global optimal routes that match the tourists’ interests within the limited time and space complexity.
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