A Collaborative Car Auto-Navigation Framework Based on Intelligent Trajectory Mining
Tong Ruan, Wang Zhan Quan, Song Tao · 2009
Vehicle trajectories are widely used in varies of applications. As to car navigation applications, typical usages of trajectories include correcting the digital maps, finding out the traffic jams, looking for the locations of cars. In this paper, it is assumed that trajectory data of expert drivers implies ldquobest practicerdquo routes, which will be helpful to naive drivers during driving. Therefore we propose a new idea called ldquocollaborative navigationrdquo in which the best practice routes are mined from trajectories and are send to naive drivers when needed. The major difficulties of this approach are that the trajectory data may be too large and the data processing time may be too long. To address the problem, we design a framework which covers the whole life cycle of trajectories processing, including original data collection, ldquopoint to routerdquo conversion, ldquobest routerdquo mining, and route query. Corresponding algorithms, data structures and data indexes are devised for each step in the life cycle. Experiments show ldquocollaborative navigationrdquo can be used to enhance routing selection with small footprint and quick response time using our framework.