A data aggregation based approach to exploit dynamic spatio-temporal correlations for citywide crowd flows prediction in fog computing

Accurate and timely predicting citywide traffic crowd flows precisely is crucial for public safety and traffic management in smart cities. Nevertheless, its crucial challenge lies in how to model multiple complicated spatial dependencies between different regions, dynamic temporal laws among different time intervals with external factors such as holidays, events, and weather. Some existing work leverage the long short-term memory (LSTM) and convolutional neural network (CNN) to explore temporal relations and spatial relations, respectively; which have outperformed the classical statistical methods. However, it is difficult for these approaches to jointly model spatial and temporal correlations. To address this problem, we propose a dynamic deep hybrid spatio-temporal neural network namely DHSTNet, to predict traffic flows in every region of a city with high accuracy. In particular, our DSHTNet model comprises four properties i.e., closeness volume, daily volume, trend volume, and external branch, respectively. Moreover, the projected model dynamically assigns different weights to various branches and, then, integrate outputs of four properties to produce final prediction outcomes. The model has been evaluated, both for offline and online predictions, using an edge/fog infrastructure where training happens on the remote cloud and prediction occurs at the edge i.e. in the proximity of users. Extensive experiments and evaluation on two real-world datasets demonstrate the advantage of the proposed model, in terms of high accuracy over prevailing state-of-the-art baseline methods. Moreover, we apply the exaggeration approach based on an attention mechanism to the above model, called as AAtt-DHSTNet; to predict citywide short-term traffic crowd flows; and show its notable performance in the traffic flows prediction. The aggregation method collects information from the related time series, remove redundancy and, thus, increases prediction speed and accuracy. Our empirical evaluation suggests that the AAtt-DHSTNet model is approximately 20.8% and 8.8% more accurate than the DHSTNet technique, for two different real-world traffic datasets.