Publication Type

Journal Article

Version

acceptedVersion

Publication Date

2-2018

Abstract

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.

Keywords

participatory sensing, Mobile crowdsourcing, uncertainty modeling, context-aware, empirical study, spatial crowdsourcing, user behavior

Discipline

Artificial Intelligence and Robotics | Computer Sciences | Operations Research, Systems Engineering and Industrial Engineering

Research Areas

Intelligent Systems and Optimization

Publication

ACM Transactions on Intelligent Systems and Technology

Volume

9

Issue

3

First Page

1

Last Page

24

ISSN

2157-6904

Identifier

10.1145/3078842

Publisher

ACM

Copyright Owner and License

LARC and Authors

Additional URL

https://doi.org/10.1145/3078842

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