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.

STAttn: SpatialTemporal Attention Mechanism for Multistep Citywide Crowd Flow Prediction Multistep citywide crowd flow prediction (MsCCFP) is to predict the inout flow of each region in a city in the given multiple consecutive periods. For traffic control and public safety protection, it can provide a long term view for taking measures. However, the spatial and temporal correlations in crowd movements and the lack of information make MsCCFP challenging. In this paper, a deeplearning based prediction model with spatialtemporal attention mechanism is proposed for MsCCFP. The model, called STAttn for short, follows the general encoderdecoder framework for modeling sequential data but adopts a multipleoutput strategy to preserve the correlations characterizing between each predicted step. The spatialtemporal attention mechanism learns to globally determine the focus on those parts of the city at certain periods that are more relevant to the predicted region and time period. Besides, a prepredicted result calculated by spatiotemporal kernel density estimation is fed to STAttn, which provides a reference for further accurate predicting. Experiments on three realworld datasets are carried out to verify STAttnu0027s performance and the results show that STAttn outperforms the baselines in terms of MsCCFP.