TripCube: A Trip-oriented vehicle trajectory data indexing structure

- TripCube is developed to address the challenge of organizing and indexing massive vehicle trajectory data.
- Based on cube-shape indexing structure, TripCube allocates indexing storage space before inserting new entries.
- TripCube is especially applicable to long-term and massive vehicle trajectory data confined to a given road network.
- Compared with two existing trajectory-segment-based indexing structures, TripCube exhibits more excellent query efficiency.

With the dramatic development of location-based services, a large amount of vehicle trajectory data are available and applied to different areas, while there are still many research challenges left, one of them being data access issues. Most of existing tree-shape indexing schemes cannot facilitate maintenance and management of very large vehicle trajectory data. How to retrieve vehicle trajectory information efficiently requires more efforts. Accordingly, this paper presents a trip-oriented data indexing scheme, named TripCube, for massive vehicle trajectory data. Its principle is to represent vehicle trajectory data as trip information records and develop a three-dimensional cube-shape indexing structure to achieve trip-oriented trajectory data retrieval. In particular, the approach is implemented and applied to vehicle trajectory data in the city of Shanghai including > 100 million locational records per day collected from about 13,000 taxis. TripCube is compared to two existing trajectory data indexing structures in our experiments, and the result exhibits that TripCube outperforms others.