Publication Type

Conference Proceeding Article

Publication Date

9-2016

Abstract

While mobile crowd-sourcing has become a game-changer for many urban operations, such as last mile logistics and municipal monitoring, we believe that the design of such crowdsourcing strategies must better accommodate the real-world behavioral preferences and characteristics of users. To provide a real-world testbed to study the impact of novel mobile crowd-sourcing strategies, we have designed, developed and experimented with a real-world mobile crowd-tasking platform on the SMU campus, called TA$Ker. We enhanced the TA$Ker platform to support several new features (e.g., task bundling, differential pricing and cheating analytics) and experimentally investigated these features via a two-month deployment of TA$Ker, involving 900 real users on the SMU campus who performed over 30,000 tasks. Our studies (i) show the benefits of bundling tasks as a combined package, (ii) reveal the effectiveness of differential pricing strategies and (iii) illustrate key aspects of cheating (false reporting) behavior observed among workers.

Keywords

Crowd-sourcing, context-aware, empirical study, User behaviour

Discipline

Artificial Intelligence and Robotics | Computer Sciences | Numerical Analysis and Scientific Computing

Research Areas

Intelligent Systems and Decision Analytics

Publication

Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Heidelberg, Germany, September 12-16, 2016

First Page

392

Last Page

402

ISBN

9781450344616

Identifier

10.1145/2971648.2971690

Publisher

ACM

City or Country

New York

Copyright Owner and License

Authors

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org/10.1145/2971648.2971690

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