Modelling and Optimization of Personalized Scenic Tourism Routes Based on Urgency

Abstract

Traditional route planning methods usually plan the “fastest” or “lowest cost” travel route for users with the goal of finding the shortest path or the lowest cost, but this method cannot meet the needs of tourism users for personalized and multifunctional travel routes. Given this phenomenon, this paper proposes a personalized route planning model based on urgency. First, the model uses the visitor’s historical tourism data and public road network data to extract their preferences, POI (point of interest) relationships, edge scenic values and other information. Then, the planned route function is determined according to the urgency value, which provides users with travel routes that accommodate their interest preferences and urgency. Finally, the improved genetic algorithm based on gene replacement and gene splicing operators is used to carry out numerical experiments on the Xi’an and Wuhan road network datasets. The experimental results show that the proposed algorithm is not only capable of planning routes with different functions for diverse users but also performs personalized route planning according to their preferences.

Navigation Route Planning for Tourism Intelligent Connected Vehicle Based on the Symmetrical Spatial Clustering and Improved Fruit Fly Optimization Algorithm

“…Compared to other optimal path algorithms, this algorithm had higher execution efficiency. Xu et al [10] proposed a personalized path planning model based on the urgency situation. The experimental results showed that this algorithm could not only plan routes with different functions for different users, but also plan personalized routes by different user preferences.…”

“…Within a tolerable range of algorithm runtime, our constructed method places more emphasis on the matching degree between POIs and tourist interests, as well as the overall cost of ICV routes. Reference [10] constructs a model based on the urgency of tourism, which has strong constraints on travel time. It is also different from the method we constructed.…”

“…Node n sub (11) is found, then a feasible path is formed, keep n sub (11) , as shown in the Figure 9(5). (6) Step 5: Select n sub (9) or n sub (10) . Node n sub (10) is found, then a feasible path is formed, keep n sub (10) , as shown in the Figure 9(6).…”

“…(6) Step 5: Select n sub (9) or n sub (10) . Node n sub (10) is found, then a feasible path is formed, keep n sub (10) , as shown in the Figure 9(6). (7) Step 6: Node n sub (12) is found, then a feasible path is formed, keep n sub (12) , as shown in the Figure 9 (1) sub n , as shown in the Figure 9(2).…”

“…Due to the different feature attributes and spatial attributes of POI, the calculated recommendation degrees are discrepant. Under the interest requirement conditions of the sample tourist, (10) P : Jinniu Wanda has the highest recommendation degree, which is 0.8532. The next is (1) P : People's Park, with a recommendation degree of 0.8496.…”