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A Stacked Generalization Framework for City Traffic Related Geospatial Data Analysis

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Web Technologies and Applications (APWeb 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9865))

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Abstract

Analyzing traffic related geospatial data often lacks in priori knowledge and encounters parameter setting problems due to the dynamic characteristics of city traffic. In this paper, we propose a pervasive, scalable framework for city traffic related geospatial data analysis based on a stacked generalization. Firstly we analyze the optimal linear combination based on stepwise iteration, and also prove its theoretical validity via error-ambiguity decomposition. Secondly we integrate six classical approaches into this framework, including linear least squares regression, autoregressive moving average, historical mean, artificial neural network, radical basis function neural network, support vector machine, and conduct experiments with a real city traffic detecting dataset. We further compare the proposed framework with other four linear combination models. It suggests that the proposed framework behaves more robust than other models both in variance and bias, showing a promising direction for city traffic related geospatial data analysis.

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Acknowledgement

This research is supported by the National Natural Science Foundation of China (41271408, 41401460), and the Chinese Postdoctoral Science Foundation (2015M581158).

Author information

Authors and Affiliations

  1. State Key Laboratory of Resources and Environmental Information System, IGSNRR, Chinese Academy of Sciences, Beijing, China

    Xiliang Liu, Li Yu, Peng Peng & Feng Lu

  2. Fujian Collaborative Innovation Center for Big Data Applications in Governments, Fuzhou, China

    Xiliang Liu

  3. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, China

    Feng Lu

Authors
  1. Xiliang Liu
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  2. Li Yu
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  3. Peng Peng
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  4. Feng Lu
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Corresponding author

Correspondence to Feng Lu .

Editor information

Editors and Affiliations

  1. University of Tsukuba , Tsukuba, Japan

    Atsuyuki Morishima

  2. East China Normal University , Shanghai, China

    Rong Zhang

  3. University of New South Wales , Sydney, New South Wales, Australia

    Wenjie Zhang

  4. The University of New South Wales, School of Computer Science and Engineering, Sydney, Australia

    Lijun Chang

  5. Advanced Digital Sciences Center, Illinois at Singapore Pte Ltd, Advanced Digital Sciences Center, Fusionopolies Way, Singapore

    Tom Z. J Fu

  6. Hyundai Motor Tower, Pivotal Inc 15/F, Hyundai Motor Tower, Chaoyang, China

    Kuien Liu

  7. Illinois at Singapore Pte Ltd, Advanced Digital Sciences Center , Singapore, Singapore

    Xiaoyan Yang

  8. South China Normal University, School of Computer Science, Guangzhou, China

    Jia Zhu

  9. Department of Computer Science, Hong Kong Baptist University Department of Computer Science, Kowloon Tong, Hong Kong

    Zhiwei Zhang

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© 2016 Springer International Publishing Switzerland

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Cite this paper

Liu, X., Yu, L., Peng, P., Lu, F. (2016). A Stacked Generalization Framework for City Traffic Related Geospatial Data Analysis. In: Morishima, A., et al. Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9865. Springer, Cham. https://doi.org/10.1007/978-3-319-45835-9_23

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  • DOI: https://doi.org/10.1007/978-3-319-45835-9_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45834-2

  • Online ISBN: 978-3-319-45835-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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