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 logis-tics tasks. Current approaches to such mobile crowd-tasking are very decentralized: a crowd-tasking platform usually pro-vides 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 ap-proach, 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 assign-ment 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 spe-cific 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
