A Hybrid Stacked Traffic Volume Prediction Approach for a Sparse Road Network How to provide accurate and timely traffic flow information has become a hot topic in recent years since they can help schedule trips better and reduce traffic congestion. In previous studies, some machine learning (ML)based models were proposed to predict the traffic volume at a single road segmentposition, and these models performed not bad. However, when applied in a more complicated road network, they show low efficiency or need to pay higher computing costs. To solve this problem, an innovative MLbased model named selected stacked gated recurrent units model (SSGRU), is proposed for predicting the traffic flow through a sparse road network in this paper. There are mainly two parts in this model, one is used to do spatial pattern mining based on linear regression coefficients, and the other one includes a stacked gated recurrent unit (SGRU) which is essential for multiroad traffic flow prediction. A binarytree is adopted to approximate the sparse road network in the suburban area. To evaluate the proposed model, seven different traffic volume data sets recorded at 15min interval are chosen from the England Highways data set to test our proposed work. The result shows that our model has greater adaptability and higher accuracy than others when applied to a multiroad input infrastructure.

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.