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Location prediction is the key technique in many location based services including route navigation, dining location recommendations, and traffic planning and control, to mention a few. This survey provides a comprehensive overview of location prediction, including basic definitions and concepts, algorithms, and applications. First, we introduce the types of trajectory data and related basic concepts. Then, we review existing location-prediction methods, ranging from temporal-pattern-based prediction to spatiotemporal-pattern-based prediction. We also discuss and analyze the advantages and disadvantages of these algorithms and briefly summarize current applications of location prediction in diverse fields. Finally, we identify the potential challenges and future research directions in location prediction.


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Location Prediction on Trajectory Data: A Review

Show Author's information Ruizhi WuGuangchun Luo( )Junming ShaoLing TianChengzong Peng
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.

Abstract

Location prediction is the key technique in many location based services including route navigation, dining location recommendations, and traffic planning and control, to mention a few. This survey provides a comprehensive overview of location prediction, including basic definitions and concepts, algorithms, and applications. First, we introduce the types of trajectory data and related basic concepts. Then, we review existing location-prediction methods, ranging from temporal-pattern-based prediction to spatiotemporal-pattern-based prediction. We also discuss and analyze the advantages and disadvantages of these algorithms and briefly summarize current applications of location prediction in diverse fields. Finally, we identify the potential challenges and future research directions in location prediction.

Keywords:

location prediction, trajectory data, data mining
Received: 20 November 2017 Accepted: 10 January 2018 Published: 12 April 2018 Issue date: June 2018
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Received: 20 November 2017
Accepted: 10 January 2018
Published: 12 April 2018
Issue date: June 2018

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© The author(s) 2018

Acknowledgements

We thank the editors and reviewer for everything you have done for us. This research was supported by the Foundation of Science & Technology Department of Sichuan Province (Nos. 2017JY0027 and 2016GZ0075), the National Key Research and Development Program (2016YFB0502300).

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