In this article, we investigate effective ways of utilizing crowdworkers in providing various urban services. The task recommendation platform that we design can match tasks to crowdworkers based on workers’ historical trajectories and time budget limits, thus making recommendations personal and efficient. One major challenge we manage to address is the handling of crowdworker’s trajectory uncertainties. In this article, we explicitly allow multiple routine routes to be probabilistically associated with each worker. We formulate this problem as an integer linear program whose goal is to maximize the expected total utility achieved by all workers. We further exploit the separable structures of the formulation and apply the Lagrangian relaxation technique to scale up computation. Numerical experiments have been performed over the instances generated using the realistic public transit dataset in Singapore. The results show that we can find significantly better solutions than the deterministic formulation, and in most cases we can find solutions that are very close to the theoretical performance limit. To demonstrate the practicality of our approach, we deployed our recommendation engine to a campus-scale field trial, and we demonstrate that workers receiving our recommendations incur fewer detours and complete more tasks, and are more efficient against workers relying on their own planning (25% more for top workers who receive recommendations). This is achieved despite having highly uncertain worker trajectories. We also demonstrate how to further improve the robustness of the system by using a simple multi-coverage mechanism.
participatory sensing, Mobile crowdsourcing, uncertainty modeling, context-aware, empirical study, spatial crowdsourcing, user behavior
Artificial Intelligence and Robotics | Computer Sciences
Intelligent Systems and Decision Analytics
ACM Transactions on Intelligent Systems and Technology
Association for Computing Machinery (ACM)
CHENG, Shih-Fen; CHEN CEN; KANDAPPU, Thivya; LAU, Hoong Chuin; MISRA, Archan; JAIMAN, Nikita; DARATAN, Randy Tandriansyah; and KOH, Ming Hui, Desmond (XU Minghui).
Scalable urban mobile crowdsourcing: Handling uncertainty in worker movement. (2017). ACM Transactions on Intelligent Systems and Technology. 9, (3), 1-24. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3888