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A Multi-dimension Weighted Graph-Based Path Planning with Avoiding Hotspots

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Knowledge Graph and Semantic Computing: Semantic, Knowledge, and Linked Big Data (CCKS 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 650))

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Abstract

With the development of industrialization rapidly, vehicles have become an important part of people’s life. However, transportation system is becoming more and more complicated. The core problem of the complicated transportation system is how to avoid hotspots. In this paper, we present a graph model based on a multi-dimension weighted graph for path planning with avoiding hotspots. Firstly, we extend one-dimension weighted graphs to multi-dimension weighted graphs where multi-dimension weights are used to characterize more features of transportation. Secondly, we develop a framework equipped with many aggregate functions for transforming multi-dimension weighted graphs into one-dimension weighted graphs in order to converse the path planning of multi-dimension weighted graphs into the shortest path problem of one-dimension weighted graphs. Finally, we implement our proposed framework and evaluate our system in some necessary practical examples. The experiment shows that our approach can provide “optimal” paths under the consideration of avoiding hotspots.

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Acknowledgements

We would like to thank Yaqi Chen for previous survey and useful comments. This work is supported by the program of the National Key Research and Development Program of China (2016YFB1000603) and the National Natural Science Foundation of China (NSFC) (61502336, 61373035). Xiaowang Zhang is supported by Tianjin Thousand Young Talents Program.

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Authors and Affiliations

  1. School of Computer Software, Tianjin University, Tianjin, 300350, China

    Shuo Jiang & Zhiyong Feng

  2. School of Computer Science and Technology, Tianjin University, Tianjin, 300350, China

    Xiaowang Zhang, Xin Wang & Guozheng Rao

  3. Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin, 300350, China

    Shuo Jiang, Zhiyong Feng, Xiaowang Zhang, Xin Wang & Guozheng Rao

Authors
  1. Shuo Jiang
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  2. Zhiyong Feng
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  3. Xiaowang Zhang
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  4. Xin Wang
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  5. Guozheng Rao
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Corresponding author

Correspondence to Shuo Jiang .

Editor information

Editors and Affiliations

  1. Zhejiang University, Zhejiang, China

    Huajun Chen

  2. Rensselaer Polytechnic Institute, Troy, New York, USA

    Heng Ji

  3. Chinese Academy of Sciences, Beijing, China

    Le Sun

  4. Google Research, Mountain View, California, USA

    Haixun Wang

  5. Wuhan University, Wuhan, Hubei, China

    Tieyun Qian

  6. East China University of Science and Technology, Shanghai, China

    Tong Ruan

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© 2016 Springer Nature Singapore Pte Ltd.

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Jiang, S., Feng, Z., Zhang, X., Wang, X., Rao, G. (2016). A Multi-dimension Weighted Graph-Based Path Planning with Avoiding Hotspots. In: Chen, H., Ji, H., Sun, L., Wang, H., Qian, T., Ruan, T. (eds) Knowledge Graph and Semantic Computing: Semantic, Knowledge, and Linked Big Data. CCKS 2016. Communications in Computer and Information Science, vol 650. Springer, Singapore. https://doi.org/10.1007/978-981-10-3168-7_2

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  • DOI: https://doi.org/10.1007/978-981-10-3168-7_2

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3167-0

  • Online ISBN: 978-981-10-3168-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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