Skip to main content
main-content
Top

Hint

Swipe to navigate through the chapters of this book

2022 | OriginalPaper | Chapter

Short-Term Demand Forecasting Methods for Public Bicycles Under Big Data Environment

Authors: Hongxiao Lin, Hui Sun

Published in: Innovative Computing

Publisher: Springer Nature Singapore

share
SHARE

Abstract

As the main form of urban slow traffic system, public bicycles are responsible for solving the “last mile” problem of urban public transportation. However, due to inadequate understanding of travel demand and deficiencies in planning and site selection, many cities still have the problem of no public bicycles to borrow or return to at peak times. The development and application of big data technology provides the possibility for refined analysis and prediction of public bicycle borrowing and repayment needs. In view of this, this paper establishes an autoregressive mobility model based on the historical data of public bicycle rental sites, and proposes a big data-based public bicycle demand forecasting method, and compares the forecast results with the forecast results of the exponential smoothing model. The results prove that, Compared with the exponential smoothing model, the autoregressive model used in this article is more accurate.

To get access to this content you need the following product:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 69.000 Bücher
  • über 500 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Testen Sie jetzt 15 Tage kostenlos.

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 50.000 Bücher
  • über 380 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




Testen Sie jetzt 15 Tage kostenlos.

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 58.000 Bücher
  • über 300 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Testen Sie jetzt 15 Tage kostenlos.

Literature
1.
go back to reference Wang B, Liu P, Chao Z et al (2018) Research on hybrid model of garlic short-term price forecasting based on big data. Comput Mater Continua 57(2):283–296 CrossRef Wang B, Liu P, Chao Z et al (2018) Research on hybrid model of garlic short-term price forecasting based on big data. Comput Mater Continua 57(2):283–296 CrossRef
2.
go back to reference Agafonov AA, Yumaganov AS, Myasnikov VV (2018) Big data analysis in a geoinformatic problem of short-term traffic flow forecasting based on a K nearest neighbors method. Comput Opt 42(6):1101–1111 CrossRef Agafonov AA, Yumaganov AS, Myasnikov VV (2018) Big data analysis in a geoinformatic problem of short-term traffic flow forecasting based on a K nearest neighbors method. Comput Opt 42(6):1101–1111 CrossRef
3.
go back to reference Li ZC, Yao MZ, Lam WHK et al (2015) Modeling the effects of public bicycle schemes in a congested multi-modal road network. Int J Sustain Transp 9(4):282–297 CrossRef Li ZC, Yao MZ, Lam WHK et al (2015) Modeling the effects of public bicycle schemes in a congested multi-modal road network. Int J Sustain Transp 9(4):282–297 CrossRef
4.
go back to reference Gao X, Lee GM (2019) Moment-based rental prediction for bicycle-sharing transportation systems using a hybrid genetic algorithm and machine learning. Comput Indus Eng 128:60–69 Gao X, Lee GM (2019) Moment-based rental prediction for bicycle-sharing transportation systems using a hybrid genetic algorithm and machine learning. Comput Indus Eng 128:60–69
5.
go back to reference Jung E, Kim AG, Kim H (2017) A study on the method of forecasting restaurant revenue using big data. Adv Sci Lett 23(3):1613–1617 CrossRef Jung E, Kim AG, Kim H (2017) A study on the method of forecasting restaurant revenue using big data. Adv Sci Lett 23(3):1613–1617 CrossRef
6.
go back to reference Lee JW, Kim HJ, Kim MK (2020) Design of short-term load forecasting based on ANN using bigdata. Trans Korean Inst Electr Eng 69(6):792–799 CrossRef Lee JW, Kim HJ, Kim MK (2020) Design of short-term load forecasting based on ANN using bigdata. Trans Korean Inst Electr Eng 69(6):792–799 CrossRef
7.
go back to reference Liu N, Zhang Q, Liu H (2015) Online short-term load forecasting based on ELM with kernel algorithm in micro-grid environment. Diangong Jishu Xuebao/Trans China Electrotechn Soc 30(8):218–224 Liu N, Zhang Q, Liu H (2015) Online short-term load forecasting based on ELM with kernel algorithm in micro-grid environment. Diangong Jishu Xuebao/Trans China Electrotechn Soc 30(8):218–224
8.
go back to reference Bo H, Nan W, Feng D et al (2015) Study on the method of enterprise short-term load forecasting considering weather and product information. Int J Control Autom 8:307–316 CrossRef Bo H, Nan W, Feng D et al (2015) Study on the method of enterprise short-term load forecasting considering weather and product information. Int J Control Autom 8:307–316 CrossRef
9.
go back to reference Kong X, Zheng F, Zhijun E et al (2018) Short-term load forecasting based on deep belief network. Dianli Xitong Zidonghua/Autom Electric Power Syst 42(5):133–139 Kong X, Zheng F, Zhijun E et al (2018) Short-term load forecasting based on deep belief network. Dianli Xitong Zidonghua/Autom Electric Power Syst 42(5):133–139
10.
go back to reference Lv Z, Shao J, Li T (2015) Short-term forecast method of hot-blast stove gas consumption trend based on PSO-BP neural network. J Inf Computationalence 12(18):6823–6833 Lv Z, Shao J, Li T (2015) Short-term forecast method of hot-blast stove gas consumption trend based on PSO-BP neural network. J Inf Computationalence 12(18):6823–6833
Metadata
Title
Short-Term Demand Forecasting Methods for Public Bicycles Under Big Data Environment
Authors
Hongxiao Lin
Hui Sun
Copyright Year
2022
Publisher
Springer Nature Singapore
DOI
https://doi.org/10.1007/978-981-16-4258-6_127

Premium Partner