Shortterm Bus Passenger Flow Forecast Based On Deep Learning The public transportation system is an essential part of the life of the citizens and itxe2x80x99s the basis of intelligent transportation system(ITS). This paper tries to predict shortterm bus passenger flow by using deep learning approach that called SAE model and DBN model. The model training and evaluation were carried out using the credit card records of the Suzhou bus IC card. The experimental results show that the SAE and DBN models can reduce the prediction error by 9.51% and 10.48%, respectively, compared with the traditional method. The methods of deep learning show a good application prospect in the shortterm bus passenger flow forecasting.

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.