TRACCS presentation
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This paper proposes TRACCS, a trajectory-aware coordinated approach for large-scale mobile crowd-tasking. TRACCS aims to maximize revenue from assigned tasks for workers while minimizing detours. It uses a centralized greedy construction heuristic followed by iterated local search to optimize task assignments. Evaluation shows TRACCS can assign over 85-90% of tasks with detours under 10% and outperforms decentralized approaches by over 20% in task completion rates and 60% lower detours on average. The paper contributes an optimization method balancing revenue and costs for large-scale location-based crowd-sourcing.
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TRACCS presentation
- 1. TRACCS: Trajectory-Aware Coordinated Urban Crowd-Sourcing Cen Chen and Shih-Fen Cheng and Aldy Gunawan and Archan Misra School of Information Systems Singapore Management University Koustuv Dasgupta and Deepthi Chander Xerox Research Centre India Presentater: Xinyang Li 2/13/2015
- 2. 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.
- 3. 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.
- 4. A quick view of paper ● Method o Decentralized → Centralized o 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
- 5. Comparation Compare with centralized algorithm ● Increase revenue of assigned tasks > 20% by increasing the task completion ratio.
- 6. Comparation Compare with centralized algorithm ● Reduce the average detour overhead by > 60%
- 7. Comparation Compare with decentralized algorithm ● The task completion ratio of decentralized is significantly lower than Greedy+ILS
- 8. Model Details Notations: ● 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.
- 9. Model Details Greedy Construction Heuristic The main goal of Greedy is to let every agent carry high-value tasks as much as possible.
- 10. Model Details Iterated 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.
- 11. Main Results
- 12. 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.
- 13. ©Xinyang Li University at Buffalo Appreaciate your attendance Happy Valentine’s Day and Chinese New Year
Editor's Notes
- Decentralized: A crowd-tasking platform usually provides each worker a set of available tasks close to the worker’s current location; and each worker then independently chooses which tasks she wants to accept the perform. Centralized: The crowd-tasking platform assigns a sequence of tasks to each worker, taking into account their expected location trajectory over a wider time horizon. Optimization Problem: so-called heuristic method, but kind of greedy algorithm, that’s why they call it Greedy-ILS. This is computationally-efficient via simulations with realistic topologies and commuting patterns. Different movement patterns can impose different types of detour constraints. And they present 2 different integer linear problem model, they are detour-with-rejoin and detour-without-retrace, to capture these diversity precisely. City-scale tasks, 1000 workers and 2000 tasks in 2 seconds.
- The remaining detour time is caculated by total time of predicted route times threshold.
- But not perfect, algorithm has to be enhanced to deal with not only addtional constraints but also more walk trajectory. But it obviously give us a idea to centrally-coordinated assign tasks not independently.