Mining Locations And Route Recommandation Based On Car Networking

More and more GPS-enabled devices are playing a part in people’s life, which is changing the way people interacting with the internet and brings us an ocean of GPS trajectories representing people’s movement from a location to another. Researchers inland and abroad did a lot of research in the direction of the user’s GPS data mining, these research have made great progress not only in the academic, but also greatly satisfying the needs of the people’s travel and other aspects of life. The main contents of this paper are data mining based on car networking GPS information.The purpose of this project is data mining based on multiple users’ GPS track in a given area of the most important sites and classic travel sequence. The important sites here are the places refer to Beijing’s Tiananmen Square and cultural attractions where people often take a visit, such as shopping malls and restaurants. This information not only can help users understand the surrounding location, but also gives the travel routes recommending space to research.In this paper, the author first build a trajectory model for multiple users’ GPS log with tree-based multi-level graph(HG). Secondly, the author proposed a referring model based on HITS algorithm and HG. The three main ideas for this model of referring location interest are: 1) the importance of the location not only depends on the number of users visiting, also depends on the travel experience of users; 2) there are mutually reinforcement between users’ travel experience and location interest. 3) location interest and users’ travel experience are only relative value, and is the area related. Finally, mining classic travel path of multiple locations based on multiple locations’ interest and many users’ travel experience.Last but not least, the author proposed a method for the verification of the algorithm presented in the paper and a data set collected by 182 drivers in five years to verify the theoretical topic. The verification results shows that the referring model based on HITS algorithm in this paper gave a better performance than the baseline algorithms, which are rank by count and rank by frequency. Moreover, algorithm in this paper for mining travel experience and location interest is better than rank by count and rank by interest alone.