A Deep Learning Spatiotemporal Prediction Framework for Mobile Crowdsourced Services
This work addresses the challenge of service availability prediction for mobile crowdsourcing platforms, but it appears incremental as it builds on existing deep learning and clustering methods.
The paper tackles the problem of predicting spatiotemporal availability of mobile crowdsourced services by introducing a two-stage deep learning framework that clusters services and formulates availability as a classification problem, achieving validated effectiveness through experiments.
This papers presents a deep learning-based framework to predict crowdsourced service availability spatially and temporally. A novel two-stage prediction model is introduced based on historical spatio-temporal traces of mobile crowdsourced services. The prediction model first clusters mobile crowdsourced services into regions. The availability prediction of a mobile crowdsourced service at a certain location and time is then formulated as a classification problem. To determine the availability duration of predicted mobile crowdsourced services, we formulate a forecasting task of time series using the Gramian Angular Field. We validated the effectiveness of the proposed framework through multiple experiments.