Two-stage greedy algorithm based on crowd sensing for tour route recommendation

Zheng, Xiaoyao; You, Hao; Huang, He; Sun, Liping; Yu, Qingying; Luo, Yonglong

doi:10.1016/j.asoc.2024.111260

Cited by 4 publications

(4 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…4.6.6. Superiority of the Proposed Algorithm Through analysis, it can be concluded that the proposed algorithm has significant advantages over the methods used in the literature [5][6][7][8][9][10][11][12] and the commonly used electronic map methods used in tour route planning, as follows:…”

Section: Analysis Of the Comparison Of Three Methodsmentioning

confidence: 99%

“…Han [8] proposed a scenic spot recommendation method, combining geographical labeled photos with time, space, and visual embedding. Zheng et al [9] proposed a two-stage greedy algorithm based on crowd sensing to solve the problem of TRR. The crowd sensing score mechanism was used to explore the locations of POIs surrounding scenic spots and the influence of user evaluations of scenic spots, by which the tour routes were planned.…”

Section: Analysis Of Related Workmentioning

confidence: 99%

“…Based on the analysis of existing studies, the proposed algorithm has significant advantages over the literature [5][6][7][8][9][10][11][12].…”

Section: Comparison With Literature Workmentioning

confidence: 99%

“…Reference [9] recommended tour routes based on crowd perception, without recommending scenic spots from the perspective of individual tourist interest and matching with feature attributes, nor did it present a complete specific algorithm for planning the lowest-cost route based on the best-matched scenic spots. The advantage of our proposed algorithm lies in recommending the best-matched scenic spots based on tourist interests, and searching for the lowest-cost routes based on the current geographical and spatial constraints of tourism cities.…”

Section: Comparison With Literature Workmentioning

confidence: 99%

See 3 more Smart Citations

Intelligent Geo-Tour Route Recommendation Algorithm Based on Feature Text Mining and Spatial Accessibility Model

Zhou,

Zhang,

Liang

et al. 2024

Electronics

View full text Add to dashboard Cite

In view of the problems in planning and recommending tour routes, this paper constructs a feature text mining (FTM) method and spatial accessibility model (SAM) as the key factors for scenic spot recommendation (SSR) and tour route recommendation (TRR). The scenic spot clustering algorithm (SSCA) based on FTM was constructed by tourists’ text evaluation data mining. Considering the spatial attributes of scenic spots, the scenic spot topology tree algorithm (SSTTA) based on dynamic buffer spatial accessibility (DBSA) was constructed. The optimal scenic spots were recommended based on interest matching and spatial accessibility optimization. As to the recommended scenic spots, this paper proposes an optimal tour route recommendation algorithm (TRRA) based on SSTTA, which aims to determine the optimal adjacent section path structure tree (ASPST) with the lowest cost under travel constraints and transportation modes. The experiment verifies that the proposed algorithm can recommend scenic spots that match tourists’ interests and have optimal spatial accessibility, and the optimal tour routes with the lowest costs under certain travel constraints. Compared with the searched sub-optimal tour routes, the optimal tour route recommended by the proposed algorithm produces the lowest travel costs, and all the scenic spots in the tour route meet the tourists’ interests. Compared with the commonly used BDMA and GDMA methods, the proposed algorithm can determine the optimal routes with lower travel costs.

show abstract