Demand Forecasting of a Public Bike-Sharing System Reflecting Dynamic Spatial Data

Lee, Jiwon; Kim, Jiyoung; Yu, Kiyun

doi:10.34726/lbs2019.51

Record link:

https://doi.org/10.34726/lbs2019.51
http://hdl.handle.net/20.500.12708/678

Title:

Demand Forecasting of a Public Bike-Sharing System Reflecting Dynamic Spatial Data

Citation:

Lee, J., Kim, J., & Yu, K. (2019). Demand Forecasting of a Public Bike-Sharing System Reflecting Dynamic Spatial Data. In G. Gartner & H. Huang (Eds.), LBS 2019 : Adjunct Proceedings of the 15th International Conference on Location-Based Services. Wien. https://doi.org/10.34726/lbs2019.51

reposiTUm DOI:

10.34726/lbs2019.51

CatalogPlus:

AC15512991

Publication Type:

Inproceedings - Full-Paper Contribution

Language:

English

Authors:

Lee, Jiwon
Kim, Jiyoung
Yu, Kiyun

Published in:

LBS 2019 : Adjunct Proceedings of the 15th International Conference on Location-Based Services

Date (published):

2019

Event name:

15th International Conference on Location-Based Services

Event date:

11-Nov-2019 - 13-Nov-2019

Event place:

Wien, Austria

Number of Pages:

Publisher:

Wien

Keywords:

Public Bike-sharing System; Social Media; Random Forest

Abstract:

Bike sharing is booming in Korea as a leisure traffic mode, but its original purpose was to reduce traffic congestion. This study developed a demand forecasting model for bike sharing connected to a subway station. For accurate demand forecasting, we used various kinds of data to reflect the spatial distribution of travel demand. We used machine learning method as prediction model, and random forest had the best predictive result.

License:

CC BY 4.0