TY - GEN
T1 - Effective recycling planning for dockless sharing bikes
AU - Zhang, Cong
AU - Li, Yanhua
AU - Bao, Jie
AU - Ruan, Sijie
AU - He, Tianfu
AU - Lu, Hui
AU - Tian, Zhihong
AU - Liu, Cong
AU - Tian, Chao
AU - Lin, Jianfeng
AU - Li, Xianen
N1 - Publisher Copyright:
© 2019 Copyright held by the owner/author(s).
PY - 2019/11/5
Y1 - 2019/11/5
N2 - Bike-sharing systems become more and more popular in the urban transportation system, because of their convenience in recent years. However, due to the high daily usage and lack of effective maintenance, the number of bikes in good condition decreases significantly, and vast piles of broken bikes appear in many big cities. As a result, it is more difficult for regular users to get a working bike, which causes problems both economically and environmentally. Therefore, building an effective broken bike prediction and recycling model becomes a crucial task to promote cycling behavior. In this paper, we propose a predictive model to detect the broken bikes and recommend an optimal recycling program based on the large scale real-world sharing bike data. We incorporate the realistic constraints to formulate our problem and introduce a flexible objective function to tune the trade-off between the broken probability and recycled numbers of the bikes. Finally, we provide extensive experimental results and case studies to demonstrate the effectiveness of our approach.
AB - Bike-sharing systems become more and more popular in the urban transportation system, because of their convenience in recent years. However, due to the high daily usage and lack of effective maintenance, the number of bikes in good condition decreases significantly, and vast piles of broken bikes appear in many big cities. As a result, it is more difficult for regular users to get a working bike, which causes problems both economically and environmentally. Therefore, building an effective broken bike prediction and recycling model becomes a crucial task to promote cycling behavior. In this paper, we propose a predictive model to detect the broken bikes and recommend an optimal recycling program based on the large scale real-world sharing bike data. We incorporate the realistic constraints to formulate our problem and introduce a flexible objective function to tune the trade-off between the broken probability and recycled numbers of the bikes. Finally, we provide extensive experimental results and case studies to demonstrate the effectiveness of our approach.
KW - Bike-sharing systems
KW - Optimal recycling program
KW - Predictive model
UR - http://www.scopus.com/inward/record.url?scp=85077009652&partnerID=8YFLogxK
U2 - 10.1145/3347146.3359340
DO - 10.1145/3347146.3359340
M3 - Conference contribution
AN - SCOPUS:85077009652
T3 - GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems
SP - 62
EP - 70
BT - 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2019
A2 - Banaei-Kashani, Farnoush
A2 - Trajcevski, Goce
A2 - Guting, Ralf Hartmut
A2 - Kulik, Lars
A2 - Newsam, Shawn
PB - Association for Computing Machinery
T2 - 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2019
Y2 - 5 November 2019 through 8 November 2019
ER -
