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

  • Yi Ouyang
  • Bin GuoEmail author
  • Xinjiang Lu
  • Qi Han
  • Tong Guo
  • Zhiwen Yu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11204)

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.

Keywords

Bike-sharing app Mobile app Competitive prediction Popularity contest Crowdsourced data 

Notes

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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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yi Ouyang
    • 1
  • Bin Guo
    • 1
    Email author
  • Xinjiang Lu
    • 1
  • Qi Han
    • 2
  • Tong Guo
    • 1
  • Zhiwen Yu
    • 1
  1. 1.Northwestern Polytechnical UniversityXi’anChina
  2. 2.Colorado School of MinesGoldenUSA

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