Data-driven smart bike-sharing system by implementing machine learning algorithms

Jia Qian*, Livio Pianura, Matteo Comin

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

This paper aims to solve a real-life problem: the bike-sharing management system arises the requirement of offering the customers the accessibility of the bikes in different bike-stations concerning the potential demands in every time-slice. The prediction of needs is critical to the distribution of the limited resources (bikes and empty slots to place the bikes) and the management of the system. We propose addressing this problem by using the regression model, which is trained by the raw data collecting from the different sensors. Thanks to the wide distribution of the edge devices, the machine learning algorithms, and the advanced computing ability, we may incorporate the intelligence to the database-related system. We will demonstrate that the boosting gradient method as a predictor to forecast the quantities of rentals and returns of bikes, outperforming the other means, e.g., random forest, support vector machine, etc. It reaches a promising result; the average accuracy reaches 75%.

Original languageEnglish
Title of host publicationProceedings of 6th International Conference on Enterprise Systems, ES 2018
PublisherIEEE
Publication date24 Dec 2018
Pages50-55
Article number8588257
ISBN (Electronic)9781538683880
DOIs
Publication statusPublished - 24 Dec 2018
Event6th International Conference on Enterprise Systems, ES 2018 - Limassol, Cyprus
Duration: 1 Oct 20182 Oct 2018

Conference

Conference6th International Conference on Enterprise Systems, ES 2018
CountryCyprus
CityLimassol
Period01/10/201802/10/2018

Keywords

  • Big data
  • Embedded system
  • Machine learning
  • Smart management system

Cite this

Qian, J., Pianura, L., & Comin, M. (2018). Data-driven smart bike-sharing system by implementing machine learning algorithms. In Proceedings of 6th International Conference on Enterprise Systems, ES 2018 (pp. 50-55). [8588257] IEEE. https://doi.org/10.1109/ES.2018.00015