Route Planning and Design of Sightseeing Vehicles in Tourist Attractions Based on Hybrid Genetic Algorithm

The development of artificial intelligence has brought new demands to the tourism industry, and intelligent planning paths will lead the development of traditional tourism. In order to solve the problems of imperfect development of the current tourist sightseeing vehicle service system and waste of resources, this study conducted an in-depth analysis of the characteristics of tourist sightseeing vehicles and the factors affecting route planning. The study comprehensively considered the operating costs and tourist satisfaction, designed targeted objective functions and three route planning design schemes, and used a hybrid genetic algorithm to solve them. The results show that after using the hybrid genetic algorithm to solve the L1 scheme and the L3 scheme, L1 is the best route planning design scheme. The total driving time of Route 1 and Route 2 is approximately 110min and 60min, and when x the value is 5, the increase in tourist satisfaction is less than the increase in total operating costs. The above results show that the research method can balance the contradiction between economic benefits and passenger needs, help sightseeing vehicle companies improve their competitiveness, and promote the green development of the tourism industry.