A Deep Learning Spatiotemporal Prediction Framework for Mobile Crowdsourced Services

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 crowdsou...

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Bibliographic Details
Published in:arXiv.org 2018-09
Main Authors: Ahmed Ben Said, Erradi, Abdelkarim, Neiat, Azadeh Ghari, Bouguettaya, Athman
Format: Article
Language:English
Subjects:
Availability
Crowdsourcing
Deep learning
Mathematical models
Prediction models
Online Access:Get full text
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Description
Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.1809.00811