TRACCS: Trajectory-Aware Coordinated Urban Crowd-Sourcing

Cen Chen and Shih-Fen Cheng and Aldy Gunawan and Archan MisraSchool of Information Systems Singapore Management University

Koustuv Dasgupta and Deepthi ChanderXerox Research Centre India

Presentater: Xinyang Li2/13/2015

What is the problem to be analyzed?Large-scale mobile crowd-tasking

A large pool of citizen crowd-workers are used to perform a variety of location-specific urban logistics tasks.

Why should we care about and what we can learn from?● Revenue

○ To maximize the total payoff from all assigned tasks for workers.● Cost

○ Reduce the detour (from the expected path) that a worker will experience to complete assigned tasks.

We can learn about the optimization method to balance the revenue and cost.

A quick view of paper● Method

o Decentralized → Centralizedo Instantaneous timestamp →

Wider time horizon (location point V.S. location trajectory)

o Optimization Problem● Contribution

o Capture diverse optimization objects precisely

o Handle city-scale tasks

ComparationCompare with centralized algorithm

● Increase revenue of assigned tasks > 20% by increasing the task completion ratio.

ComparationCompare with centralized algorithm

● Reduce the average detour overhead by > 60%

ComparationCompare with decentralized algorithm

● The task completion ratio of decentralized is significantly lower than Greedy+ILS

Model DetailsNotations:

● N is set of all nodes. Nt is task node.● M is set of all workers(agents).● ti,j is traveling time from i to j.● st is reward.● Rm is the set of routine nodes of agent m.● pi

m is itended visit sequence of Rm.

Model DetailsGreedy Construction HeuristicThe main goal of Greedy is to let every agent carry high-value tasks as much as possible.

Model DetailsIterated Local Search(ILS)The main goal of ILS is to find best task with highest utility revenue and lowest detour time and then modify the task routine of agents or between agents. It improves the solution generated by Greedy algorithm with SWAP, MOVE, INSERT, REPLACE operations.

Main Results

Conclusions● Greedy+ILS heuristic is able to assign over 85-90% of the total tasks with

detours threshold no higher than 10%.● Centrally-coordinated heuristics significantly outperform the present

Myopic approach of independent, achieving 20% or higher task assignment rates, and reducing the average detour overhead by 60% or higher.

©Xinyang Li University at Buffalo

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