Using Machine Learning Methods to Predict Demand for Bike Sharing

  • Gao C
  • Chen Y
N/ACitations
Citations of this article
21Readers
Mendeley users who have this article in their library.
Get full text

Abstract

This open access book presents the proceedings of the International Federation for IT and Travel & Tourism (IFITT)’s 29th Annual International eTourism Conference, which assembles the latest research presented at the ENTER2022 conference, which will be held on January 11–14, 2022. The book provides an extensive overview of how information and communication technologies can be used to develop tourism and hospitality. It covers the latest research on various topics within the field, including augmented and virtual reality, website development, social media use, e-learning, big data, analytics, and recommendation systems. The readers will gain insights and ideas on how information and communication technologies can be used in tourism and hospitality. Academics working in the eTourism field, as well as students and practitioners, will find up-to-date information on the status of research.

Cite

CITATION STYLE

APA

Gao, C., & Chen, Y. (2022). Using Machine Learning Methods to Predict Demand for Bike Sharing. In Information and Communication Technologies in Tourism 2022 (pp. 282–296). Springer International Publishing. https://doi.org/10.1007/978-3-030-94751-4_25

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free