SMTP: An Optimized Storage Method for Vehicle Trajectory Data Exploiting Trajectory Patterns

Hailun Wang, Mingming Zhang, Renyu Yang, Xuelian Lin, Tianyu Wo, Rajiv Ranjan, Jie Xu
2016 2016 IEEE 18th International Conference on High Performance Computing and Communications; IEEE 14th International Conference on Smart City; IEEE 2nd International Conference on Data Science and Systems (HPCC/SmartCity/DSS)  
Recent advances in location-acquisition and mobile sensing technologies have enabled tracking of vehicle movements (i.e., trajectory data). Massive trajectory datasets are processed routinely (often in real-time) to provide support for many new types of IoV (Internet of Vehicles) applications (e.g., traffic congestion management, and load-coordination across electric vehicle charging stations). High-volume, high-velocity data emitted by IoV applications introduces issues with efficient spatial
more » ... nd temporal queries over massively redundant datasets, typically represented as a collection of longitude-latitude tuples. In this paper we present SMTP, a new storage method based on the recognition of trajectory patterns to reduce the storage space for the trajectory data. An adaptive algorithm for mining trajectory patterns from the data is developed, and it recognizes frequent trajectories as patterns according to the geo-space relationships between trajectories. A combinatorial optimization algorithm is then introduced to decide which trajectory patterns should be used for trajectory storage, thereby removing redundant data and saving space. The recognized and saved patterns also help to accelerate queries to the trajectory data. Several large IoV datasets from the real world are used to validate the effectiveness of the proposed method. Experimental results show that storage space for trajectory data can be reduced by 38% while a typical query to the data can be accelerated by approximately 40%.
doi:10.1109/hpcc-smartcity-dss.2016.0112 dblp:conf/hpcc/WangZYLWRX16 fatcat:rh2i2l3mabhwdbdpaig5qal3ka