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SMOPAT: Mining semantic mobility patterns from trajectories of private vehicles.

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Title: SMOPAT: Mining semantic mobility patterns from trajectories of private vehicles.
Authors: Wan, Chengcheng1, Zhu, Yanmin1 yzhu@sjtu.edu.cn, Yu, Jiadi1, Shen, Yanyan1
Source: Information Sciences. Mar2018, Vol. 429, p12-25. 14p.
Subjects: Data mining, Semantic Web, Information theory, Global Positioning System, Dynamic programming
Abstract: With the increasing use of private vehicles with positioning services, GPS trajectory data of vehicles has become one of the major sources of big data about urban life. Existing studies on mobility pattern mining from trajectories share a common limitation, i.e., they fail to capture the semantics of trajectories. Automatic derivation of semantic information for every trajectory is a challenging task. In this paper, we propose an approach, called SMOPAT (Semantic MObility PATterns), for mining spatial-temporal semantic mobility patterns from trajectories of private vehicles. We design a probabilistic generative model with latent variables to characterize the semantic mobility of vehicles. Based on the model, SMOPAT labels each location in a trajectory with a visit purpose by using a polynomial-time dynamic programming algorithm. It then employs an efficient algorithm to find the most frequent semantic mobility patterns. We evaluate our approach on a large data set of real trajectories of private vehicles spanning a time duration of over ten months with 114 million records in Shanghai, China. The experimental results show that our approach produces meaningful patterns and outperforms the two competing methods in terms of diversity, coherence, and coverage. [ABSTRACT FROM AUTHOR]
Copyright of Information Sciences is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 127076150
AccessLevel: 6
PubType: Periodical
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PreciseRelevancyScore: 0
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  Data: <searchLink fieldCode="AR" term="%22Wan%2C+Chengcheng%22">Wan, Chengcheng</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Zhu%2C+Yanmin%22">Zhu, Yanmin</searchLink><relatesTo>1</relatesTo><i> yzhu@sjtu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Yu%2C+Jiadi%22">Yu, Jiadi</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Shen%2C+Yanyan%22">Shen, Yanyan</searchLink><relatesTo>1</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Information+Sciences%22">Information Sciences</searchLink>. Mar2018, Vol. 429, p12-25. 14p.
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  Data: With the increasing use of private vehicles with positioning services, GPS trajectory data of vehicles has become one of the major sources of big data about urban life. Existing studies on mobility pattern mining from trajectories share a common limitation, i.e., they fail to capture the semantics of trajectories. Automatic derivation of semantic information for every trajectory is a challenging task. In this paper, we propose an approach, called SMOPAT (Semantic MObility PATterns), for mining spatial-temporal semantic mobility patterns from trajectories of private vehicles. We design a probabilistic generative model with latent variables to characterize the semantic mobility of vehicles. Based on the model, SMOPAT labels each location in a trajectory with a visit purpose by using a polynomial-time dynamic programming algorithm. It then employs an efficient algorithm to find the most frequent semantic mobility patterns. We evaluate our approach on a large data set of real trajectories of private vehicles spanning a time duration of over ten months with 114 million records in Shanghai, China. The experimental results show that our approach produces meaningful patterns and outperforms the two competing methods in terms of diversity, coherence, and coverage. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Group: Ab
  Data: <i>Copyright of Information Sciences is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.ins.2017.10.043
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      – Code: eng
        Text: English
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        PageCount: 14
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      – SubjectFull: Data mining
        Type: general
      – SubjectFull: Semantic Web
        Type: general
      – SubjectFull: Information theory
        Type: general
      – SubjectFull: Global Positioning System
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      – SubjectFull: Dynamic programming
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      – TitleFull: SMOPAT: Mining semantic mobility patterns from trajectories of private vehicles.
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            NameFull: Zhu, Yanmin
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            NameFull: Yu, Jiadi
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            – D: 01
              M: 03
              Text: Mar2018
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              Y: 2018
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