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Mobile crowdsourcing has become easier thanks to the widespread ofsmartphones capable of seamlessly collecting and pushing the desired data tocloud services. However, the success of mobile crowdsourcing relies onbalancing the supply and demand by first accurately forecasting spatially andtemporally the supply-demand gap, and then providing efficient incentives toencourage participant movements to maintain the desired balance. In this paper,we propose Deep-Gap, a deep learning approach based on residual learning topredict the gap between mobile crowdsourced service supply and demand at agiven time and space. The prediction can drive the incentive model to achieve ageographically balanced service coverage in order to avoid the case where someareas are over-supplied while other areas are under-supplied. This allowsanticipating the supply-demand gap and redirecting crowdsourced serviceproviders towards target areas. Deep-Gap relies on historical supply-demandtime series data as well as available external data such as weather conditionsand day type (e.g., weekday, weekend, holiday). First, we roll and encode thetime series of supply-demand as images using the Gramian Angular SummationField (GASF), Gramian Angular Difference Field (GADF) and the Recurrence Plot(REC). These images are then used to train deep Convolutional Neural Networks(CNN) to extract the low and high-level features and forecast the crowdsourcedservices gap. We conduct comprehensive comparative study by establishing twosupply-demand gap forecasting scenarios: with and without external data.Compared to state-of-art approaches, Deep-Gap achieves the lowest forecastingerrors in both scenarios.

Deep-Gap: A deep learning framework for forecasting crowdsourcing supply-demand gap based on imaging time series and residual learning