Mining popular routes from social media

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


研究成果: Chapter同行評審

3 引文 斯高帕斯(Scopus)


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.

主出版物標題Multimedia Data Mining and Analytics
主出版物子標題Disruptive Innovation
發行者Springer International Publishing
出版狀態Published - 1 1月 2015


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