Discovering Frequent Movement Paths From Taxi Trajectory Data Using Spatially Embedded Networks and Association Rules

In view of the complex traffic flows, spatial interactions within a city exhibit the properties of dynamics, connectivity, and repeatability. This paper aims at mining spatial-temporal movement patterns from massive taxi trajectory data for discovering the inherent travel flow information within the urban system. Similar to the role of ocean circulation in a marine system, identifying the frequent paths and cycles of the travel flows within a city would be critical for understanding the principles behind the travel flow surfaces. Thus, we propose a multi-level method for the discovery of movement paths by incorporating the techniques of network analysis and association rules. Specifically, the proposed method begins by constructing a directed network on the subdivision of the study region, in which the node with geolocation represents the corresponding cell and the edge with weight represents the travel flow between neighboring cells. The method then adopts an extended label propagation clustering algorithm to identify frequent paths and cycles on the flow network within a specific time interval. Finally, to extract frequent paths during the whole time period, we also develop an association rules mining algorithm by modeling the edges as items and the paths in each time span as transactions. Experiment results demonstrate that our framework is able to effectively mine movement patterns in taxi trajectory data. Our results are expected to provide an avenue for further research, such as transportation planning and urban structure analysis.