Development of Android Application for City Tour Recommendation System Based on Dynamic Programming

Nugroho, Benni Agung; Izzah, Abidatul; Widyastuti, Ratna

doi:10.1109/siet48054.2019.8986055

Cited by 3 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 4(b) shows the calculation results after introducing the two-way search algorithm into the traditional greedy algorithm. e forward search result of the two-way search algorithm is the same as that of the traditional greedy algorithm, and the path obtained from the reverse search meets the path obtained from the forward search at the grid with coordinates (5,4). e nal path planning result can be obtained by combining the paths obtained from forward and reverse search.…”

Section: Improved Algorithmmentioning

confidence: 99%

“…The tourist route chosen is a significant consideration that should be considered prior to travel [ 2 , 3 ]. Optimizing the tourist route can reduce the cost of travel, improve the efficiency of tourism, and ensure that people enjoy a better experience in tourism [ 4 , 5 ]. At present, the design of tourism routes adheres to the concept of “freedom, individuality, exploration, and mastery.” Based on these design concepts, the original itineraries need to be properly optimized to ensure the growing tourist purposefulness [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improved On-Demand Travel Route Planning Model with Interest Fields

Li-min

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Intelligent tourism route planning is an important element of smart tourism, and the current tourism route planning has problems such as strong subjectivity and low personalization considering tourists’ interests. To solve the problems of current tourism route planning, an improved interest field travel route planning model is proposed. Firstly, an intelligent interest field extraction model is established. Secondly, an improved greedy algorithm is designed to reduce the risk of missing the optimal solution, strengthen the local search capability, and improve the solution accuracy of the algorithm. The extracted routes of interest sites are planned, and a motivated iterative value output model is established. The experimental results demonstrate that the selected routes are shorter and less expensive than the traditional model. By iterating the actual data to obtain the iterative values of different tourist route motivations and the sequential guide map of attractions based on tourist interests, the optimal and suboptimal routes that satisfy the tourist motivation interests are analyzed. This model has strong feasibility and practical significance for smart tourism route planning.

show abstract

Section: Improved Algorithmmentioning

confidence: 99%