Visualizing Hidden Themes of Trajectories with Semantic Transformation

Ding Chu, David A. Sheets, Ye Zhao, Yingyu Wu, Maogong Zheng, George Chen, Jing Yang
A new methodology, semantic transformation of taxi trajectory, is developed to discover and analyze the hidden knowledge of massive taxi trajectories. This approach creatively transforms the geographic coordinates (i.e. latitude and longitude) to textual remarks. Consequently, each taxi trajectory is studied as a document consisting of semantic information, such as the taxi traversed street names, which enables semantic analysis of massive taxi data sets as document corpora. Hidden themes, namely taxi topics, are identified through topic modeling techniques. The taxi topics reflect urban mobility patterns and trends, which are displayed and analyzed through a visual analytics system. The system integrates interactive visualization tools such as taxi topic maps, topic routes, street clouds, and parallel coordinates to visualize the probability-based topical information. Urban planners, administration, travelers, and drivers can conduct various knowledge discovery tasks with direct semantic and visual assists. The effectiveness of this approach is illustrated by case studies using a large taxi trajectory data set acquired from 21,360 taxis in a city.