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Prediction Technology for Parking Occupancy Based on Multi-dimensional Spatial-Temporal Causality and ANN Algorithm

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Green, Pervasive, and Cloud Computing (GPC 2020)

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Abstract

The Granger causality model is extended by supplementing spatial, weather, and other factors. Therefore, a multi-dimensional spatial-temporal causality model for the prediction of the parking occupancy is proposed, and the prediction algorithm for parking occupancy based on multi-dimensional spatial-temporal causality and ANN is carefully designed. The CityPulse dataset provided by the European Union FP7 project is introduced to train the network, and verify our algorithm. The experimental results show that our new technology for prediction of parking occupancy can effectively improve the accuracy of the prediction, compared with other algorithms only rely on time or spatial factors.

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Acknowledgement

This work is supported by the National Nature Science Foundation of China (no. 61602016), and Beijing Science and Technology Project (no. D171100004017003). The authors would like to acknowledge Dr. Ruihai Dong in School of Computer Science, University College Dublin for improving the language in the article.

Author information

Authors and Affiliations

  1. Beijing Advanced Innovation Center for Future Internet Technology, Beijing, 100124, China

    Jian He

  2. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

    Jiahao Bai

Authors
  1. Jian He
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  2. Jiahao Bai
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Correspondence to Jiahao Bai .

Editor information

Editors and Affiliations

  1. Northwestern Polytechnical University, Xi’an, China

    Zhiwen Yu

  2. University of Mannheim, Mannheim, Germany

    Christian Becker

  3. Chinese University of Hong Kong, Shatin, Hong Kong

    Guoliang Xing

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He, J., Bai, J. (2020). Prediction Technology for Parking Occupancy Based on Multi-dimensional Spatial-Temporal Causality and ANN Algorithm. In: Yu, Z., Becker, C., Xing, G. (eds) Green, Pervasive, and Cloud Computing. GPC 2020. Lecture Notes in Computer Science(), vol 12398. Springer, Cham. https://doi.org/10.1007/978-3-030-64243-3_19

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  • DOI: https://doi.org/10.1007/978-3-030-64243-3_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64242-6

  • Online ISBN: 978-3-030-64243-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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