The Study On Optimization Of Self-driving Tourism Service System In Yangtze River Delta

The arrival of the automotive era, the self-driving tourism had a vigorous development, the size of the market showed explosive growth. However, the absence of self driving tourism services and the mismatch problem is becoming increasingly apparent, the supply of tourism services can not meet the growing and upgrading of the tourists’ demand. The service of scientific theory, supply and demand equilibrium theory, customer perception theory based, using content analysis method, text analysis method and semantic network analysis, on the Yangtze River Delta region, the self-driving tourism service supply system and status quo were summarized and analyzed; through analysis and interpretation of the text of network travels, excavate self-driving tourists travel service demand system and specific requirements. In the course of the comparative study of supply and demand, it is concluded that the mismatch between supply and demand in the 3 aspects of value, structure and factors of self-driving tourism service system. Finally, the paper puts forward the countermeasures and suggestions for the specific problems in 3 aspects.The main research results of this paper show that: there is a clear mismatch between the demand of self-driving tourists in the Yangtze River Delta region and the current tourism service supply system. Among them, the value of supply and demand, leading products supply system unable to meet the needs of tourists; supply and demand structure, supply side, disorderly combination of the status quo and self-driving tourists’ demand system exists obvious difference; elements of supply and demand, in terms of quantity, quality, time and space are lack or imbalance. It also inspired a Yangtze River Delta selfdriving tourism service system must be led from the product optimization of service-oriented, from the disorderly development portfolio optimization for the orderly development of combination, from the unilateral supply optimization for multi collaborative supply system.