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Linking granular computing, big data and decision making: a case study in urban path planning

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Abstract

Granular computing, an emerging information processing paradigm transforming complex data into information granules at different scales so that different features and regularities can be revealed, offers an essential linkage between big data and decision making. By using innovative technologies of granular computing that transforms big data collections into information granules, we would be at position of recognizing and exploiting the meaningful pieces of knowledge present in data, and produce sound, and practically supported decisions. In this study, we first summarize a general scheme of big data–granular computing–decision making and then present a case study where we detect the important traffic event information by collecting and analyzing social media data, and transform them into probabilistic information granules that can be used for urban routing navigation. We propose a robust fastest path optimization model to incorporate the impact of traffic events and generate the optimal routing strategy. Real-life experiments are carried out in regional Chaoyang District, Beijing, as well as the backbone roadway network of Beijing, which illustrate the effectiveness of our proposed big data-driven decision-making method. Our study provides new evidence demonstrating that big data can be efficiently used to enhance decisions and granular computing with this regard. The concept of the proposed scheme can be easily extended for decision-making modeling in other domains.

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Notes

  1. Decisive action: How businesses make decisions and how they could do it better, The Economist Intelligence Unit, 2014. http://www.datascienceassn.org/.

  2. http://open.weibo.com/.

  3. http://ictclas.nlpir.org/.

  4. http://news.baidu.com.

  5. http://www.datatang.com/data/15775.

  6. http://www.keenage.com/.

  7. http://open.weibo.com/.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 71722007, 71931001), and the Funds for First-class Discipline Construction in China (XK1802-5).

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Authors and Affiliations

  1. School of Economics and Management, Beijing University of Chemical Technology, Beijing, 100029, China

    Xiang Li

  2. School of Data Science, City University of Hong Kong, Hong Kong, China

    Jiandong Zhou

  3. Department of Electrical and Engineering, University of Alberta, Edmonton, AB, T6R 2V4, Canada

    Witold Pedrycz

  4. Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

    Witold Pedrycz

  5. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Witold Pedrycz

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  1. Xiang Li
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  2. Jiandong Zhou
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Correspondence to Xiang Li.

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Li, X., Zhou, J. & Pedrycz, W. Linking granular computing, big data and decision making: a case study in urban path planning. Soft Comput 24, 7435–7450 (2020). https://doi.org/10.1007/s00500-019-04369-6

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