Modelling the publishing process of big location data using deep learning prediction methods

Yan Yan*, Bingqian Wang, Quan Z. Sheng, Adnan Mahmood, Tao Feng, Pengshou Xie

*Corresponding author for this work

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Abstract

Centralized publishing of big location data can provide accurate and timely information to assist in traffic management and for facilitating people to decide travel time and route, mitigate traffic congestion, and reduce unnecessary waste. However, the spatio-temporal correlation, non-linearity, randomness, and uncertainty of big location data make it impossible to decide an optimal data publishing instance through traditional methods. This paper, accordingly, proposes a publishing interval predicting method for centralized publication of big location data based on the promising paradigm of deep learning. First, the adaptive adjusted sampling method is designed to address the challenge of finding a reasonable release time via a prediction mechanism. Second, the Maximal Overlap Discrete Wavelet Transform (MODWT) is introduced for the decomposition of time series in order to separate different features of big location data. Finally, different deep learning models are selected to construct the entire framework according to various time-domain features. Experimental analysis suggests that the proposed prediction scheme is not only feasible, but also improves the prediction accuracy in contrast to the traditional deep learning mechanisms.

Original languageEnglish
Article number420
Number of pages21
JournalElectronics (Switzerland)
Volume9
Issue number3
DOIs
Publication statusPublished - Mar 2020

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Bibliographical note

Copyright the Author(s) 2020. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • Adaptive adjusted sampling
  • Big location data
  • Deep learning
  • MODWT decomposition
  • Publishing interval prediction

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