A graph-based approach to vehicle trajectory analysis

作者：Guo Diansheng^*; Liu Shufan; Jin Hai

来源：Journal of Location Based Services, 2010, 4(3): 183-199.

DOI：10.1080/17489725.2010.537449

• Clustering
• Complex networks
• Data redundancy
• Data sets
• Existing method
• Graph partitioning
• Graph-based
• Graph-based representations
• matrix
• New approaches
• Novel patterns
• Regionalisation
• Representative point
• Spatiotemporal patterns
• Topological relations
• Topological relationships
• Trajectory analysis
• Trajectory data
• Vehicle movements
• Vehicle trajectories

It is difficult to extract meaningful patterns from massive trajectory data. One of the main challenges is to characterise, compare and generalize trajectories to find overall patterns and trends. The major limitation of existing methods is that they do not consider topological relations among trajectories. This research proposes a graph-based approach that converts trajectory data to a graph-based representation and treats them as a complex network. Within the context of vehicle movements, the research develops a sequence of steps to extract representative points to reduce data redundancy, interpolate trajectories to accurately establish topological relationships among trajectories and locations, construct a graph (or matrix) representation of trajectories, apply a spatially constrained graph partitioning method to discover natural regions defined by trajectories and use the discovered regions to search and visualise trajectory clusters. Applications with a real data set shows that our new approach can effectively facilitate the understanding of spatial and spatiotemporal patterns in trajectories and discover novel patterns that existing methods cannot find. ? 2010 Taylor & Francis.

• 出版日期2010