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.
@article{arxiv.1809.00811,
title = {A Deep Learning Spatiotemporal Prediction Framework for Mobile Crowdsourced Services},
author = {Ahmed Ben Said and Abdelkarim Erradi and Azadeh Ghari Neiat and Athman Bouguettaya},
journal= {arXiv preprint arXiv:1809.00811},
year = {2018}
}