Xiao Chen
ABSTRACT
Crowdsourcing has inspired a variety of novel mobile applications. However, identifying common practices across different applications is still challenging. In this paper, we use smart parking as a case study to investigate features of crowdsourcing that may apply to other mobile applications. Based on this we derive principles for efficiently harnessing crowdsourcing. We draw three key guidelines: First, we suggest that that the organizer can play an important role in coordinating participants', a key factor to successful crowdsourcing experience. Second, we suggest that the expected participation rate is a key factor when designing the crowdsourcing system: a system with a lower expected participation rate will place a higher burden in individual participants (e.g., through more complex interfaces that aim to improve the accuracy of the collected data). Finally, we suggest that not only above certain threshold of contributors, a crowdsourcing-based system is resilient to freeriding but, surprisingly, that including freeriders (i.e., actors that do not participate in system effort but share its benefits in terms of coordination) benefits the entire system.
- D.C. Brabham, T.W. Sanchez and K. Bartholomew, "Crowdsourcing Public Participation in Transit Planning: Preliminary Results from Next Stop Design Case," in TRB 89th Annual Meeting Compendium of Papers DVD, 2010.Google Scholar
- A. Sorokin and D. Forsyth, "Utility data annotation with Amazon Mechanical Turk," in Computer Vision and Pattern Recognition Workshops, 2008. CVPRW '08. IEEE Computer Society Conference on, pp. 1--8, 2008.Google Scholar
- P. White, "No Vacancy: Park Slopes Parking Problem And How to Fix It," Http://www.Transalt.org/newsroom/releases/126, .Google Scholar
- Sarah Kessler, "How Smarter Parking Technology Will Reduce Traffic Congestion," Http://mashable.com/2011/04/13/smart-Parking-Tech/, 2011.Google Scholar
- Tingxin Yan, Baik Hoh, Deepak Ganesan, Ken Tracton, Toch Iwuchukwu, and Juong-Sik Lee., "CrowdPark:A Crowdsourcing-based Parking Reservation System for Mobile Phones." UMASS Technical Report., Tech. Rep. UM-CS-2011-001, 2011.Google Scholar
- Waze, Http://www.Waze.Com/, .Google Scholar
- GasBuddy, "Find Low Gas Prices in the USA and Canada," Http://gasbuddy.Com, .Google Scholar
- J. Kincaid, "Googles Open Spot Makes Parking A Breeze, Assuming Everyone Turns Into A Good Samaritan." Http://techcrunch.com/2010/07/09/google-Parking-Open-Spot/, .Google Scholar
- Bin Li, Daqing Zhang, Lin Sun, Chao Chen, Shijian Li, Guande Qi and Qiang Yang, "Hunting or waiting? Discovering passenger-finding strategies from a large-scale real-world taxi dataset," in Pervasive Computing and Communications Workshops (PERCOM Workshops), 2011 IEEE International Conference on, pp. 63--68, 2011.Google Scholar
- N. Lamba, "Social Media Trackles Traffic," Http://www.Wired.com/autopia/2010/12/ibm-Thoughts-on-a-Smarter-Planet-8/, 2010.Google Scholar
- S.S. Kanhere, "Participatory Sensing: Crowdsourcing Data from Mobile Smartphones in Urban Spaces," in Mobile Data Management (MDM), 2011 12th IEEE International Conference on, pp. 3--6, 2011. Google ScholarDigital Library
- S. Reddy, A. Parker, J. Hyman, J. Burke, D. Estin and M. Hansen, "Image Browsing, Processing and Clustering for Participatory Sensing: Lessons from a DietSense Prototype," in Proceedings of the Workshop on Embedded Networked Sensors (EmNetS), Cork, Ireland, June 2007, . Google ScholarDigital Library
- M. Mun, S. Reddy, et al, "PEIR, the Personal Environmental Impact Report, as a Platform for Participatory Sensing Systems Research," in Proceedings of ACM MobiSys, Krakow, Poland, June 2009, . Google ScholarDigital Library
- E. Miluzzo, N. Lane, K. Fodor, R. Peterson, S. Eisenman, H. Lu, M. Musolesi, X. Zheng, A. Campbell, "Sensing Meets Mobile Social Networks: The Design, Implementation and Evaluation of the CenceMe Application," in Proceedings of ACM SenSys, Raleigh, NC, USA, November 2008. . Google ScholarDigital Library
- Jatuporn Chinrungrueng, Udomporn Sunantachaikul and Satien Triamlumlerd, "Smart Parking: An Application of Optical Wireless Sensor Network," in Applications and the Internet Workshops, 2007. SAINT Workshops 2007. International Symposium on, pp. 66--66, 2007. Google ScholarDigital Library
- Rongxing Lu, Xiaodong Lin, Haojin Zhu and Xuemin Shen, "SPARK: A New VANET-Based Smart Parking Scheme for Large Parking Lots," in INFOCOM 2009, IEEE, pp. 1413--1421, 2009.Google Scholar
- Yanfeng Geng and C.G. Cassandras, "A new "smart parking" system based on optimal resource allocation and reservations," in Intelligent Transportation Systems (ITSC), 2011 14th International IEEE Conference on, pp. 979--984, 2011.Google Scholar
- SFMTA, "SFPark- About the Project," Http://sfpark.org/about-the-Project/, .Google Scholar
- M. Caliskan, A. Barthels, B. Scheuermann and M. Mauve, "Predicting Parking Lot Occupancy in Vehicular Ad Hoc Networks," in Vehicular Technology Conference, 2007. VTC2007-Spring. IEEE 65th, pp. 277--281, 2007.Google Scholar
- Anonymous "Parking Meter Rates and Time Limits," Http://vancouver.ca/vanmap/p/parkingMeter.Htm, .Google Scholar
- Michael Behrisch, Laura Bieker, Jakob Erdmann and Daniel Krajzewicz., "SUMO - Simulation of Urban MObility: An Overview," in SIMUL 2011, The Third International Conference on Advances in System Simulation, 2011.Google Scholar
- Flavia W. K. Tsang, Amer S. Shalaby and Eric J. Miller, "Improved modeling of park-and-ride transfer time: Capturing the within-day dynamics," Journal of Advanced Transportation, vol. 39, pp. 117--137, 2005.Google ScholarCross Ref
Index Terms
- Crowdsourcing for on-street smart parking
Recommendations
Designing Human Behavior Through Social Influence in Mobile Crowdsourcing with Micro-communities
Electronic Government and the Information Systems PerspectiveAbstractThis paper proposes a new mobile social media infrastructure for motivating collective people to participate in flourishing our society. For motivating them, designing human behavior through social influence within communities is one of the most ...
Crowdsourcing GO: Effect of Worker Situation on Mobile Crowdsourcing Performance
CHI '17: Proceedings of the 2017 CHI Conference on Human Factors in Computing SystemsThe increasing popularity of mobile crowdsourcing platforms has enabled crowd workers to accept jobs wherever/whenever they are, and also provides opportunity for task requesters to order time/location specific tasks to workers. Since workers on mobile ...
Mobile crowdsourcing in the wild: challenges from a global community
MobileHCI '18: Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services AdjunctRecent research has been devoted to designing mobile applications that encourage users to complete microtasks in everyday context, known as "mobile crowdsourcing". In this case study, we present our ongoing effort of a publicly-available mobile ...
Comments