TY - GEN
T1 - Engaging Drivers in Ride Hailing via Competition
T2 - 22nd IEEE International Conference on Mobile Data Management (MDM 2021)
AU - Chengi, Hao
AU - Wed, Shuyue
AU - Zhang, Lingyu
AU - Zhou, Zimu
AU - Tongi, Yongxin
PY - 2021
Y1 - 2021
N2 - Sustained work enthusiasms of drivers are crucial for the success of large-scale ride-hailing platforms. In this paper, we conduct the first-of-its-kind exploration to encourage active participation of drivers via competition. We design Arena, a competition where drivers compete for prizes via completing more trips. Through a pilot study covering over 2,600 participants, we uncover the easy-win problem, an overlooked and serious issue in competition design for real-world drivers. It refers to situations where one competitor does not show up during competition whereas the other easily wins. To solve the easy-win problem without impairing motivation of drivers, we devise a novel prediction-based matchmaking framework. On observing that no-shows are highly correlated to the online time of drivers during competition, we propose to identify potential no-shows by predicting drivers' online time and avoid matching potential noshow drivers with drivers that will show up so as to reduce easy-wins. We conduct large-scale experiments based on real competition data involving over 10,000 drivers. The results show that our prediction-based matchmaking scheme can effectively reduce the ratio of easy-wins. © 2021 IEEE.
AB - Sustained work enthusiasms of drivers are crucial for the success of large-scale ride-hailing platforms. In this paper, we conduct the first-of-its-kind exploration to encourage active participation of drivers via competition. We design Arena, a competition where drivers compete for prizes via completing more trips. Through a pilot study covering over 2,600 participants, we uncover the easy-win problem, an overlooked and serious issue in competition design for real-world drivers. It refers to situations where one competitor does not show up during competition whereas the other easily wins. To solve the easy-win problem without impairing motivation of drivers, we devise a novel prediction-based matchmaking framework. On observing that no-shows are highly correlated to the online time of drivers during competition, we propose to identify potential no-shows by predicting drivers' online time and avoid matching potential noshow drivers with drivers that will show up so as to reduce easy-wins. We conduct large-scale experiments based on real competition data involving over 10,000 drivers. The results show that our prediction-based matchmaking scheme can effectively reduce the ratio of easy-wins. © 2021 IEEE.
KW - Case Study
KW - Competition
KW - Spatial Crowdsourcing
UR - https://www.scopus.com/pages/publications/85112348829
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85112348829&origin=recordpage
U2 - 10.1109/MDM52706.2021.00016
DO - 10.1109/MDM52706.2021.00016
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781665428453
T3 - Proceedings - IEEE International Conference on Mobile Data Management
SP - 19
EP - 28
BT - Proceedings - 2021 22nd IEEE International Conference on Mobile Data Management
PB - IEEE
Y2 - 15 June 2021 through 18 June 2021
ER -