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

Yi Ouyang; Bin Guo; Xinjiang Lu; Qi Han; Tong Guo; Zhiwen Yu

doi:10.1007/978-3-030-15093-8_17

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

Yi Ouyang
, Bin Guo
, Xinjiang Lu
, Qi Han
, Tong Guo
, Zhiwen Yu

School of Computer Science

Northwestern Polytechnical University Xian
Colorado School of Mines

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Green, Pervasive, and Cloud Computing - 13th International Conference, GPC 2018, Revised Selected Papers
Editors	Shijian Li
Publisher	Springer Verlag
Pages	241-255
Number of pages	15
ISBN (Print)	9783030150921
DOIs	https://doi.org/10.1007/978-3-030-15093-8_17
State	Published - 2019
Event	13th International Conference on Green, Pervasive, and Cloud Computing, GPC 2018 - Hangzhou, China Duration: 11 May 2018 → 13 May 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11204 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th International Conference on Green, Pervasive, and Cloud Computing, GPC 2018
Country/Territory	China
City	Hangzhou
Period	11/05/18 → 13/05/18

Keywords

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

Access to Document

10.1007/978-3-030-15093-8_17

Cite this

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 S. Li (Ed.), Green, Pervasive, and Cloud Computing - 13th International Conference, GPC 2018, Revised Selected Papers (pp. 241-255). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11204 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-15093-8_17

Ouyang, Yi ; Guo, Bin ; Lu, Xinjiang et al. / CompetitiveBike : Competitive Prediction of Bike-Sharing Apps Using Heterogeneous Crowdsourced Data. Green, Pervasive, and Cloud Computing - 13th International Conference, GPC 2018, Revised Selected Papers. editor / Shijian Li. Springer Verlag, 2019. pp. 241-255 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{f88212a963f149dda24e5d77c7eec082,

title = "CompetitiveBike: Competitive Prediction of Bike-Sharing Apps Using Heterogeneous Crowdsourced Data",

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, Competitive prediction, Crowdsourced data, Mobile app, Popularity contest",

author = "Yi Ouyang and Bin Guo and Xinjiang Lu and Qi Han and Tong Guo and Zhiwen Yu",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 13th International Conference on Green, Pervasive, and Cloud Computing, GPC 2018 ; Conference date: 11-05-2018 Through 13-05-2018",

year = "2019",

doi = "10.1007/978-3-030-15093-8\_17",

language = "英语",

isbn = "9783030150921",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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pages = "241--255",

editor = "Shijian Li",

booktitle = "Green, Pervasive, and Cloud Computing - 13th International Conference, GPC 2018, Revised Selected Papers",

}

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 S Li (ed.), Green, Pervasive, and Cloud Computing - 13th International Conference, GPC 2018, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11204 LNCS, Springer Verlag, pp. 241-255, 13th International Conference on Green, Pervasive, and Cloud Computing, GPC 2018, Hangzhou, China, 11/05/18. https://doi.org/10.1007/978-3-030-15093-8_17

CompetitiveBike: Competitive Prediction of Bike-Sharing Apps Using Heterogeneous Crowdsourced Data. / Ouyang, Yi; Guo, Bin; Lu, Xinjiang et al.
Green, Pervasive, and Cloud Computing - 13th International Conference, GPC 2018, Revised Selected Papers. ed. / Shijian Li. Springer Verlag, 2019. p. 241-255 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11204 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - CompetitiveBike

T2 - 13th International Conference on Green, Pervasive, and Cloud Computing, GPC 2018

AU - Ouyang, Yi

AU - Guo, Bin

AU - Lu, Xinjiang

AU - Han, Qi

AU - Guo, Tong

AU - Yu, Zhiwen

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - Bike-sharing app

KW - Competitive prediction

KW - Crowdsourced data

KW - Mobile app

KW - Popularity contest

UR - https://www.scopus.com/pages/publications/85064044439

U2 - 10.1007/978-3-030-15093-8_17

DO - 10.1007/978-3-030-15093-8_17

M3 - 会议稿件

AN - SCOPUS:85064044439

SN - 9783030150921

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 241

EP - 255

BT - Green, Pervasive, and Cloud Computing - 13th International Conference, GPC 2018, Revised Selected Papers

A2 - Li, Shijian

PB - Springer Verlag

Y2 - 11 May 2018 through 13 May 2018

ER -

Ouyang Y, Guo B, Lu X, Han Q, Guo T, Yu Z. CompetitiveBike: Competitive Prediction of Bike-Sharing Apps Using Heterogeneous Crowdsourced Data. In Li S, editor, Green, Pervasive, and Cloud Computing - 13th International Conference, GPC 2018, Revised Selected Papers. Springer Verlag. 2019. p. 241-255. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-15093-8_17

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

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Keywords

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