Research on the Travel Route Based on Optimization Schedule

In recent years, with the development of computer sciences, computer technology has been applied to comprehensive fields. As the quality of people's life increasing, tourism businesses are developing very fast in China, and new tourist attractions are coming out in many places. The management of the data becomes an interesting topic. When people travel they often need to search for the optimal combination of sight spots and the corresponding best travel tour, the shortest driving directions in detail between any two points, the best bus path between any two points in a city, and so on. Now people can get from a travel agency or search online in the web for tourism route, but these routes are usually not optimal and made from the experiences. There are also some services for people searching for the bus path in a city, but this is often limited, and does not work for any two points in a city. For the shortest driving directions between any two points, we still do not have this kind of services in the range of the whole country, though people in the U. S. Are used to searching online for driving directions before going out. In this paper, we try to solve all these problems. We propose several algorithms to design the optimal combination of sight spots by filtering tourist attractions, and the main method is by listing all maximal cliques. We then compute the optimal travel route on this combination. This will help people to choose their attractions pots to visit and get the best tour. For other problems which people often encounter in travelling such a show to get the shortest driving directions between any two points, and optimal bus path when using public transportations, we discuss the existing algorithms and propose the practical solutions. All algorithms proposed in this paper are suitable for large scale settings such as applying to millions of tourism sight spots to list all optimal tours etc. The key point is to deal with very large amount of data, for which we must adopt high performance computing. At the end of the paper,computational results are discussed and concluded that our algorithms and designs are suitable for real applications and this will make it much easier for peoples' life.