2015 | OriginalPaper | Chapter
Mining Dependencies Considering Time Lag in Spatio-Temporal Traffic Data
Authors : Xiabing Zhou, Haikun Hong, Xingxing Xing, Wenhao Huang, Kaigui Bian, Kunqing Xie
Published in: Web-Age Information Management
Publisher: Springer International Publishing
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Learning dependency structure is meaningful to characterize causal or statistical relationships. Traditional dependencies learning algorithms only use the same time stamp data of variables. However, in many real-world applications, such as traffic system and climate, time lag is a key feature of hidden temporal dependencies, and plays an essential role in interpreting the cause of discovered temporal dependencies. In this paper, we propose a method for mining dependencies by considering the time lag. The proposed approach is based on a decomposition of the coefficients into products of two-level hierarchical coefficients, where one represents feature-level and the other represents time-level. Specially, we capture the prior information of time lag in spatio-temporal traffic data. We construct a probabilistic formulation by applying some probabilistic priors to these hierarchical coefficients, and devise an expectation-maximization (EM) algorithm to learn the model parameters. We evaluate our model on both synthetic and real-world highway traffic datasets. Experimental results show the effectiveness of our method.