Mining popular routes from social media

Ling Yin Wei*, Yu Zheng, Wen-Chih Peng

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

3 Scopus citations


The advances in location-acquisition technologies have led to a myriad of spatial trajectories. These trajectories are usually generated at a low or an irregular frequency due to applications’ characteristics or energy saving, leaving the routes between two consecutive points of a single trajectory uncertain (called an uncertain trajectory). In this paper, we present a Route Inference framework based on Collective Knowledge (abbreviated as RICK) to construct the popular routes from uncertain trajectories. Explicitly, given a location sequence and a time span, the RICK is able to construct the top-k routes which sequentially pass through the locations within the specified time span, by aggregating such uncertain trajectories in a mutual reinforcement way (i.e., uncertain+uncertain→certain). Our work can benefit trip planning, traffic management, and animalmovement studies. The RICK comprises two components: routable graph construction and route inference. First, we explore the spatial and temporal characteristics of uncertain trajectories and construct a routable graph by collaborative learning among the uncertain trajectories. Second, in light of the routable graph, we propose a routing algorithm to construct the top-k routes according to a user-specified query. We have conducted extensive experiments on two real datasets, consisting of Foursquare check-in datasets and taxi trajectories. The results show that RICK is both effective and efficient.

Original languageEnglish
Title of host publicationMultimedia Data Mining and Analytics
Subtitle of host publicationDisruptive Innovation
PublisherSpringer International Publishing
Number of pages24
ISBN (Electronic)9783319149981
ISBN (Print)9783319149974
StatePublished - 1 Jan 2015


  • Collaborative learning
  • Route inference
  • Social media
  • Trajectory data mining


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