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arXiv · 1809.00811

A Deep Learning Spatiotemporal Prediction Framework for Mobile Crowdsourced Services

Ahmed Ben Said·Abdelkarim Erradi·Azadeh Ghari Neiat·Athman Bouguettaya
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Abstract

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.

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Ahmed Ben Said, Abdelkarim Erradi, Azadeh Ghari Neiat, Athman Bouguettaya. 2018-09-04. A Deep Learning Spatiotemporal Prediction Framework for Mobile Crowdsourced Services. https://doi.org/10.1007/s11036-018-1105-0

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