Simultaneous Arrival Matching for New Spatial Crowdsourcing Platforms
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Main track. Pages 1279-1287. https://doi.org/10.24963/ijcai.2020/178
In recent years, 3D spatial crowdsourcing platforms become popular, in which users and workers travel together to their assigned workplaces for services, such as InterestingSport and Nanguache. A typical problem over 3D spatial crowdsourcing platforms is to match users with suitable workers and workplaces. Existing studies all ignored that the workers and users assigned to the same workplace should arrive almost at the same time, which is very practical in the real world. Thus, in this paper, we propose a new Simultaneous Arrival Matching (SAM), which enables workers and users to arrive at their assigned workplace within a given tolerant time. We find that the new considered arriving time constraint breaks the monotonic additivity of the result set. Thus, it brings a large challenge in designing effective and efficient algorithms for the SAM. We design Sliding Window algorithm and Threshold Scanning algorithm to solve the SAM. We conduct the experiments on real and synthetic datasets, experimental results show the effectiveness and efficiency of our algorithms.
Data Mining: Mining Spatial, Temporal Data
Data Mining: Other