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

Abstract

The intelligent connected vehicle (ICV) decision-making system needs to match tourist interests and search for the route with the lowest travel cost when recommending POIs (Points of Interest) and navigation tour routes. In response to this research objective, we construct a navigation route-planning model for tourism intelligent connected vehicles based on symmetrical spatial clustering and improved fruit fly optimization algorithm. Firstly, we construct the POI feature attribute clustering algorithm based on the spatial decision forest to achieve the optimal POI recommendation. Secondly, we construct the POI spatial attribute clustering algorithm based on the SA-AGNES (Spatial Accessibility-Agglomerative Nesting) to achieve the spatial modeling between POIs and ICV clusters. On the basis of POI feature attribute and spatial attribute, we construct the POI recommendation algorithm for the ICV navigation routes based on the attribute weights. On the basis of the recommended POIs, we construct the tourism ICV navigation route-planning model based on the improved fruit fly optimization algorithm. Experiments prove that the proposed algorithm can accurately output POIs that match tourists’ interests and needs, and find out the ICV navigation route with the lowest travel cost. Compared with the commonly used map route-planning methods and traditional route-searching algorithms, the proposed algorithm can reduce the travel costs by 15.22% at most, which can also effectively reduce the energy consumption of the ICV system, and improve the efficiency of sight-seeing and traveling for tourists.