  Mobile crowdsourcing has become easier thanks to the widespread of
smartphones capable of seamlessly collecting and pushing the desired data to
cloud services. However, the success of mobile crowdsourcing relies on
balancing the supply and demand by first accurately forecasting spatially and
temporally the supply-demand gap, and then providing efficient incentives to
encourage participant movements to maintain the desired balance. In this paper,
we propose Deep-Gap, a deep learning approach based on residual learning to
predict the gap between mobile crowdsourced service supply and demand at a
given time and space. The prediction can drive the incentive model to achieve a
geographically balanced service coverage in order to avoid the case where some
areas are over-supplied while other areas are under-supplied. This allows
anticipating the supply-demand gap and redirecting crowdsourced service
providers towards target areas. Deep-Gap relies on historical supply-demand
time series data as well as available external data such as weather conditions
and day type (e.g., weekday, weekend, holiday). First, we roll and encode the
time series of supply-demand as images using the Gramian Angular Summation
Field (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 crowdsourced
services gap. We conduct comprehensive comparative study by establishing two
supply-demand gap forecasting scenarios: with and without external data.
Compared to state-of-art approaches, Deep-Gap achieves the lowest forecasting
errors in both scenarios.
