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CompetitiveBike: Competitive Prediction of Bike-Sharing Apps Using Heterogeneous Crowdsourced Data

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Green, Pervasive, and Cloud Computing (GPC 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11204))

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Abstract

In recent years, bike-sharing systems have been deployed in many cities, which provide an economical lifestyle. With the prevalence of bike-sharing systems, a lot of companies join the market, leading to increasingly fierce competition. To be competitive, bike-sharing companies and app developers need to make strategic decisions for mobile apps development. Therefore, it is significant to predict and compare the popularity of different bike-sharing apps. However, existing works mostly focus on predicting the popularity of a single app, the popularity contest among different apps has not been well explored yet. In this paper, we aim to forecast the popularity contest between Mobike and Ofo, two most popular bike-sharing apps in China. We develop CompetitiveBike, a system to predict the popularity contest among bike-sharing apps. Moreover, we conduct experiments on real-world datasets collected from 11 app stores and Sina Weibo, and the experiments demonstrate the effectiveness of our approach.

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Notes

  1. 1.

    https://en.wikipedia.org/wiki/Mobike.

  2. 2.

    https://en.wikipedia.org/wiki/Ofo_(bike_sharing).

  3. 3.

    Data from Google Play is more sparse than these app stores as Mobike and Ofo users are mainly from China, so we did not collect data from Google Play.

  4. 4.

    https://weibo.com/.

  5. 5.

    https://github.com/isnowfy/snownlp.

  6. 6.

    https://github.com/fxsjy/jieba.

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Acknowledgments

This work was partially supported by the National Key R&D Program of China (No. 2017YFB1001800), and the National Natural Science Foundation of China (No. 61332005, 61772428, 61725205).

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Authors and Affiliations

  1. Northwestern Polytechnical University, Xi’an, Shaanxi, China

    Yi Ouyang, Bin Guo, Xinjiang Lu, Tong Guo & Zhiwen Yu

  2. Colorado School of Mines, Golden, CO, USA

    Qi Han

Authors
  1. Yi Ouyang
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  2. Bin Guo
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  3. Xinjiang Lu
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  4. Qi Han
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  5. Tong Guo
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Corresponding author

Correspondence to Bin Guo .

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Editors and Affiliations

  1. Zhejiang University, Hangzhou, China

    Shijian Li

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Ouyang, Y., Guo, B., Lu, X., Han, Q., Guo, T., Yu, Z. (2019). CompetitiveBike: Competitive Prediction of Bike-Sharing Apps Using Heterogeneous Crowdsourced Data. In: Li, S. (eds) Green, Pervasive, and Cloud Computing. GPC 2018. Lecture Notes in Computer Science(), vol 11204. Springer, Cham. https://doi.org/10.1007/978-3-030-15093-8_17

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  • DOI: https://doi.org/10.1007/978-3-030-15093-8_17

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  • Print ISBN: 978-3-030-15092-1

  • Online ISBN: 978-3-030-15093-8

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