DeepGap: A Deep Learning Framework for Forecasting Crowdsourcing SupplyDemand Gap Based on Imaging Time Series and Residual Learning 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 supplydemand gap, and then providing efficient incentives to encourage participant movements to maintain the desired balance. In this paper, we propose DeepGap, 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 oversupplied while other areas are undersupplied. This allows anticipating the supplydemand gap and redirecting crowdsourced service providers towards target areas. DeepGap relies on historical supplydemand 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 supplydemand 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 highlevel features and forecast the crowdsourced services gap. We conduct comprehensive comparative study by establishing two supplydemand gap forecasting scenarios: with and without external data. Compared to stateofart approaches, DeepGap achieves the lowest forecasting errors in both scenarios.

Leveraging SpatioTemporal Patterns for Predicting Citywide Traffic Crowd Flows Using Deep Hybrid Neural Networks Predicting the accurate traffic crowd flows is of practical importance for intelligent transportation systems (ITS). However, it is challenging because traffic flows are affected by multiple complex factors, such as spatial and temporal dependencies of regions and external factors. In this paper, we propose a deep hybrid spatiotemporal dynamic neural network, called DHSTNet, to predict both inflows and outflows in every region of a city. More specifically, it consists of four main components, i.e., closeness influence taking the instantaneous variations of traffic flows, period influence regularly identifying daily changes of traffic crowd flows, weekly component identifying the patterns of weekly traffic flows and external component acquiring external factors. We design a branch of deep hybrid recurrent convolutional neural network units to model the first three temporal properties, i.e., closeness, period influence, and weekly influence. The external components are feed into two fully connected neural networks. For different branches, our proposed model assigns different weights and then combines the output of the four components. Experimental results based on two largescale realworld datasets demonstrate the superiority of our model over the existing stateoftheart methods.