Short Term Traffic Flow Forecast Based on CMGRU Networks Intelligent transportation systems (ITS) have developed for a long time. The rise of deep learning has brought new vitality of the ITS. However, traffic flow data is usually timecorrelated and highly randomized. The data distribution will also change dynamically. To actualize the forecasting of traffic flow accurately, we use the historical traffic information to predict the messages of the traffic flow at any time interval. This paper proposes an efficient traffic flow forecast architecture based on deep learning. The method combines the gated recurrent unit (GRU, a type of recurrent neural network) layers and onedimension convolution layers. Since the performance of these models has a strong dependence on hyperparameters, this paper conducts a largescale search of the hyperparameter space. At the same time, experiments on flow data show that the method proposed in this paper can achieve a better prediction accuracy. Experiments also show lower test errors compared with the existing approaches.

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.