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Scalable Urban Mobile Crowdsourcing
2017
ACM Transactions on Intelligent Systems and Technology
In this work, 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 challenging we manage to address is the handling of crowdworker's trajectory uncertainties. In this work, we explicitly allow multiple routine routes to be probabilistically associated
doi:10.1145/3078842
fatcat:uwwqtgybcfamxbmwbn5adlgt2m