Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

11-2014

Abstract

We investigate the problem of large-scale mobile crowd-tasking, where a large pool of citizen crowd-workers are used to perform a variety of location-specific urban logistics tasks. Current approaches to such mobile crowd-tasking are very decentralized: a crowd-tasking platform usually provides each worker a set of available tasks close to the worker's current location; each worker then independently chooses which tasks she wants to accept and perform. In contrast, we propose TRACCS, a more coordinated task assignment approach, where the crowd-tasking platform assigns a sequence of tasks to each worker, taking into account their expected location trajectory over a wider time horizon, as opposed to just instantaneous location. We formulate such task assignment as an optimization problem, that seeks to maximize the total payoff from all assigned tasks, subject to a maximum bound on the detour (from the expected path) that a worker will experience to complete her assigned tasks. We develop credible computationally-efficient heuristics to address this optimization problem (whose exact solution requires solving a complex integer linear program), and show, via simulations with realistic topologies and commuting patterns, that a specific heuristic (called Greedy-ILS) increases the fraction of assigned tasks by more than 20%, and reduces the average detour overhead by more than 60%, compared to the current decentralized approach.

Keywords

crowdsourcing, mobile crowdsourcing, orienteering problem, centralized planning, mobile tasking

Discipline

Artificial Intelligence and Robotics | Operations Research, Systems Engineering and Industrial Engineering | Software Engineering

Publication

Proceedings of the Second AAAI Conference on Human Computation and Crowdsourcing HCOMP 2014: November 2-4, 2014, Pittsburgh

First Page

30

Last Page

40

ISBN

9781577356820

Publisher

AAAI Press

City or Country

Palo Alto, CA

Additional URL

http://www.aaai.org/ocs/index.php/HCOMP/HCOMP14/paper/view/8966

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