Demand estimation of public bike-sharing system based on temporal and spatial correlation

Xiawen Yao, Xingfa Shen, Tian He, Sang Hyuk Son

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Nowadays, public Bike-Sharing Systems (BSSs) are broadly deployed in many cities around the world. It is important to obtain accurate user demand of BSS for better system planning and bicycle scheduling. The actual user demand includes not only the users who are served, but also those who are not served by BSS. In this study, we take into account the situations that users are not served for the first time. We propose a three-step demand estimation model to infer the situations that users are not served from both the temporal and spatial correlation, based on the two characteristics of station usage, long-term stability and shortterm volatility. The demand estimation model proposed is evaluated based on Washington D.C. bike-sharing system and uses the comprehensive information of three datasets, user trip data, station status data, and station location data. Compared with the ground truth of user demand, the minimum relative error in the experimental results of the entire system is 45.5%.

Original languageEnglish (US)
Title of host publicationProceedings - 2018 4th International Conference on Big Data Computing and Communications, BIGCOM 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages60-65
Number of pages6
ISBN (Electronic)9781538680216
DOIs
StatePublished - Oct 9 2018
Event4th International Conference on Big Data Computing and Communications, BIGCOM 2018 - Chicago, United States
Duration: Aug 7 2018Aug 9 2018

Publication series

NameProceedings - 2018 4th International Conference on Big Data Computing and Communications, BIGCOM 2018

Other

Other4th International Conference on Big Data Computing and Communications, BIGCOM 2018
Country/TerritoryUnited States
CityChicago
Period8/7/188/9/18

Bibliographical note

Funding Information:
ACKNOWLEDGMENT This work was supported by the National Science Foundation of China (NSFC) under Grants No.61672198, 61772551 and Zhejiang Provincial Natural Science Foundation under Grant No.LY14F020047, LS17G03001, and DGIST Global Research Laboratory Program (2013K1A1A2A02078326), Funded by the Korean MSIP (No.B0101-15-0557, Resilient Cyber-Physical Systems Research).

Publisher Copyright:
© 2018 IEEE.

Keywords

  • Bike-sharing system
  • Demand estimation
  • Spatial correlation
  • Temporal correlation

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