TY - GEN
T1 - Using spatial, temporal, and external factors to enhance prediction of shared-transport users
AU - Chang, Ting Hsuan
AU - Chiu, Sheng Min
AU - Chen, Yi Chung
AU - Lee, Chiang
N1 - Funding Information:
This work was supported in part by Ministry of Science and Technology Taiwan, grant number MOST 107-2119-M-224-003-MY3, MOST 110-2121-M-224-001, MOST 110-2221-E-006-176, MOST 108-2621-M-006-007-, MOST 111-2121-M-224-001, and MOST 111-2221-E-006-187-MY2.
Publisher Copyright:
© 2022 ACM.
PY - 2022/10/29
Y1 - 2022/10/29
N2 - Shared transportation, which allows commuters to share vehicles, either through riding in the same vehicle (i.e., ride-sharing) or using the same vehicle at different times (i.e., car-sharing or bike-sharing) has become increasingly popular. Car-sharing and bike-sharing require efficient allocation of vehicle resources to sharing stations. Scholars have used temporal or spatial information to predict the number of users at each station. However, external factors, such as special events or rain, can affect this number. This paper proposes a framework to improve the prediction of shared-transport users based on both temporal and spatial factors as well as the external factors of the surrounding environment of the station, the weather, and relevant online activity. The proposed approach was verified through the application to the real-world case of bicycle-sharing in Taipei, Taiwan.
AB - Shared transportation, which allows commuters to share vehicles, either through riding in the same vehicle (i.e., ride-sharing) or using the same vehicle at different times (i.e., car-sharing or bike-sharing) has become increasingly popular. Car-sharing and bike-sharing require efficient allocation of vehicle resources to sharing stations. Scholars have used temporal or spatial information to predict the number of users at each station. However, external factors, such as special events or rain, can affect this number. This paper proposes a framework to improve the prediction of shared-transport users based on both temporal and spatial factors as well as the external factors of the surrounding environment of the station, the weather, and relevant online activity. The proposed approach was verified through the application to the real-world case of bicycle-sharing in Taipei, Taiwan.
UR - http://www.scopus.com/inward/record.url?scp=85145569824&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85145569824&partnerID=8YFLogxK
U2 - 10.1145/3561278.3561282
DO - 10.1145/3561278.3561282
M3 - Conference contribution
AN - SCOPUS:85145569824
T3 - ACM International Conference Proceeding Series
SP - 5
EP - 11
BT - Proceedings of the 9th Multidisciplinary International Social Networks Conference, MISNC 2022
PB - Association for Computing Machinery
T2 - 9th Multidisciplinary International Social Networks Conference, MISNC 2022
Y2 - 29 October 2022 through 31 October 2022
ER -