Scenic tourism route plans are usually generated by combining scenic Points of Interest (PoIs) and the scenic road network. Traditional algorithms map the road networks linking the PoIs into a route collection and build a corresponding graph model. However, a single PoI description mechanism for scenic spots with multiple entrances and exits is significantly different from the actual tour route, which has multiple entrances and exits. Furthermore, the preferences and needs of tourists are not considered in attraction selection in existing algorithms. In this study, we propose a double-weighted graph model that considers the multiple entrances and exits of the PoI and identifies the tourists’ preferences using social network data. According to tourists’ different preferences and demands, different optimized tourist routes closer to the actual optimal paths were generated through an ant colony algorithm based on the proposed double-weighted graph model. To address the efficiency of the proposed model, we applied it in Shanghai and compared it with the traditional model through the 2bulu app, which can record three-dimensional (3D) trajectories of tourists. The comparison results show that the proposed model using social network data is closer to the actual 3D trajectory than the traditional model.
Finding Frequent Subgraphs in Longitudinal Social Network Data Using a Weighted Graph Mining Approach
