TY - GEN
T1 - Demand estimation of public bike-sharing system based on temporal and spatial correlation
AU - Yao, Xiawen
AU - Shen, Xingfa
AU - He, Tian
AU - Son, Sang Hyuk
PY - 2018/10/9
Y1 - 2018/10/9
N2 - 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%.
AB - 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%.
KW - Bike-sharing system
KW - Demand estimation
KW - Spatial correlation
KW - Temporal correlation
UR - http://www.scopus.com/inward/record.url?scp=85056447847&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85056447847&partnerID=8YFLogxK
U2 - 10.1109/BIGCOM.2018.00016
DO - 10.1109/BIGCOM.2018.00016
M3 - Conference contribution
AN - SCOPUS:85056447847
T3 - Proceedings - 2018 4th International Conference on Big Data Computing and Communications, BIGCOM 2018
SP - 60
EP - 65
BT - Proceedings - 2018 4th International Conference on Big Data Computing and Communications, BIGCOM 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Conference on Big Data Computing and Communications, BIGCOM 2018
Y2 - 7 August 2018 through 9 August 2018
ER -