TY - GEN
T1 - Combining contribution interactions to increase coverage in mobile participatory sensing systems
AU - Huang, Yun
AU - Zimmerman, John
AU - Tomasic, Anthony
AU - Steinfeld, Aaron
N1 - Publisher Copyright:
© 2016 ACM.
PY - 2016/9/6
Y1 - 2016/9/6
N2 - Participatory sensing systems use people and their smartphones as a sensing infrastructure, and getting people to make contributions remains a critical challenge. Little work details how system designers should combine different interactions to increase coverage of service location. Tiramisu, a participatory sensing system, invites transit riders to crowdsource real-time arrival information by sharing location traces when they commute. We extended this system with a new feature that allows riders at stops to "spot" buses passing by. To better understand the impact of this new feature, we conducted an observational log analysis, examining changes in coverage and user behavior before and after the new feature. Following the addition of the spotting feature, participants' contributions increased coverage (the number of trips with real-time data) by 98%, and they used the app more than twice as much. The addition of the spotting feature was also followed by a significant increase of trace contributions.
AB - Participatory sensing systems use people and their smartphones as a sensing infrastructure, and getting people to make contributions remains a critical challenge. Little work details how system designers should combine different interactions to increase coverage of service location. Tiramisu, a participatory sensing system, invites transit riders to crowdsource real-time arrival information by sharing location traces when they commute. We extended this system with a new feature that allows riders at stops to "spot" buses passing by. To better understand the impact of this new feature, we conducted an observational log analysis, examining changes in coverage and user behavior before and after the new feature. Following the addition of the spotting feature, participants' contributions increased coverage (the number of trips with real-time data) by 98%, and they used the app more than twice as much. The addition of the spotting feature was also followed by a significant increase of trace contributions.
KW - Coverage of service location
KW - Mobile crowdsourcing
KW - Participatory sensing
KW - User contribution
UR - http://www.scopus.com/inward/record.url?scp=84991287627&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84991287627&partnerID=8YFLogxK
U2 - 10.1145/2935334.2935387
DO - 10.1145/2935334.2935387
M3 - Conference contribution
AN - SCOPUS:84991287627
T3 - Proceedings of the 18th International Conference on Human-Computer Interaction with Mobile Devices and Services, MobileHCI 2016
SP - 365
EP - 376
BT - Proceedings of the 18th International Conference on Human-Computer Interaction with Mobile Devices and Services, MobileHCI 2016
PB - Association for Computing Machinery, Inc
T2 - 18th International Conference on Human-Computer Interaction with Mobile Devices and Services, MobileHCI 2016
Y2 - 6 September 2016 through 9 September 2016
ER -