Self-Driving Tour Travel Route Planning Model Construction Using Recurrent Neural Network

Abstract

With the continuing increase of people’s living standards, tourism has now become the primary mode of holiday and leisure activity. Scientific and reasonable route planning can best meet people’s essential needs, provide detailed planning in terms of travel time and cost, resolve the impact of various external factors, and realize the complementary benefits of scenic tourism while ensuring that each factor configuration is optimized. External factors such as trip time, cost, travel date, and transport mode all have an impact on route planning. In order to develop a mathematical model and gather other information, it is frequently essential to determine two or more of these influencing elements. The ideal plan for the influencing elements is to first develop an optimal self-driving trip route that meets all of the requirements. This strategy is becoming increasingly popular among the general public, and it focuses on how to spend the least amount of money in order to visit more tourist attractions, construct the most reasonable routes, and provide circumstances for tourists to save time and money. The purpose of this study is to examine the topic of self-driving travel route planning and to suggest the creation of a route planning model. In this paper, a self-driving journey route planning model is built using a recurrent neural network model. The driving route is designed with a high-speed priority strategy, and the travel route selection problem takes into account the consumption condition as well as the quantity of scenic sites. The developed model is used to optimize self-driving trip routes, and the results are promising.