Article Preview
TopIntroduction
Freedom is the “soul” of self-driving travel. For tourists, the self-driving tour itself is for the convenience of travel and can meet their free needs. But for the city, the test is whether the product is sticky. Therefore, the development of self-driving tours must be guided by consumer demand. Retaining self-driving tourists requires not only the transformation and upgrading of scenic spots, but also the overall image of a self-driving destination to attract car owners, and concentrate more product formats for tourists to consume and enjoy, so as to improve tourist satisfaction.
Self-driving tours are organized and planned with self-driving cars as the main means of travel. The rise of self-driving tours conforms to the psychology of the younger generation. They are unwilling to be restrained and pursue personality, the independence and freedom of mind, self-driving tour just fills this demand.
Self-driving tour is a type of self-guided tour, it is a new type of tour that is different from the traditional group tour. Self-driving travel provides tourists with flexible space in terms of object selection, participation procedures, and experience freedom. Self-driving tours, with their inherent characteristics of freedom and individuality, flexibility and comfort, choice and seasonality, are radically different from traditional participation. Compared with the collective way, it has its own characteristics and charm.
The literal meaning of self-driving is that the driver is himself. Vehicles include cars, mainly cars, off-road vehicles, RVs (Recreational Vehicle), motorcycles and bicycles. They are mainly privately owned, these can also be borrowed, leased and other methods. The driving purpose is with diversity and arbitrariness, the final decision lies with the car owner or travel team. It can be seen that tourism is one of self-driving activities. When self-driving is used as a means of travel, there will be the following changes. The driver can be the owner or his companion. The main purpose of driving is leisure travel, not for work, transportation and other reasons. Self-driving tours are private tours and are not public tours.
With the improvement of the national economy, the number of private cars has increased rapidly, self-driving travel has gradually become a popular choice for people to travel. By analyzing the self-driving trajectory of tourists, it is possible to discover popular self-driving travel routes and provide support for travelers' travel route planning. However, the activities of self-driving tourists have high autonomy, it is difficult to collect travel trajectory data, the representativeness and coverage of the data are insufficient. Regarding these, different scholars have done research. A seasonality measurement framework is constructed from pattern and intensity, the methods such as single-index panel data cluster analysis and linear programming are used to explore the seasonal temporal and spatial characteristics of China's self-driving travel (cross-city) market (Li X., 2021). A self-driving travel plan design is proposed based on distribution uniformity adaptive ant colony algorithm (Song M J, & Wu Y H.,2021). Ant colony algorithm is a probabilistic simulated evolutionary algorithm, it is used to find the optimal path in the graph, the distribution uniformity of the solution is introduced in the optimization process, the information update strategy is dynamically adjusted and the path probability is selected, it can accelerate the convergence while avoiding precociousness, and get a more reasonable self-driving tour route. This provides a reasonable solution for the self-driving tour route planning. The self-driving tour in the Silk Road is taked as the research topic, the ROST CM6 software is used to analyze a total of 12,962 pieces of information of online travel notes extracted from Ctrip. Based on this, an ASEB strategy matrix is formed for the experience of self-driving tourists on the Silk Road (Lei Li X Z, et al.,2021). ASEB is an acronym for activity, setting, experience, and benefit. A novel rough-fuzzy best-worst method is proposed to prioritize the identified requirements, simultaneously manipulating the intrapersonal and interpersonal uncertainties (Chen Z H, et al., 2020). The case study results of smart vehicle service system show that sixteen smart service requirements are identified in the self-driving tour with the smart vehicle, and the requirement “alerting the driver's unsafe behavior using informative diagnostic capability” emerges as the most important one in the proposed rough-fuzzy best-worst method.