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Traffic forecasting plays an important role in urban planning. Deep learning methods outperform traditional traffic flow forecasting models because of their ability to capture spatiotemporal characteristics of traffic conditions. However, these methods require high-quality historical traffic data, which can be both difficult to acquire and non-comprehensive, making it hard to predict traffic flows at the city scale. To resolve this problem, we implemented a deep learning method, SceneGCN, to forecast traffic speed at the city scale. The model involves two steps: firstly, scene features are extracted from Google Street View (GSV) images for each road segment using pretrained Resnet18 models. Then, the extracted features are entered into a graph convolutional neural network to predict traffic speed at different hours of the day. Our results show that the accuracy of the model can reach up to 86.5% and the Resnet18 model pretrained by Places365 is the best choice to extract scene features for traffic forecasting tasks. Finally, we conclude that the proposed model can predict traffic speed efficiently at the city scale and GSV images have the potential to capture information about human activities. 

Real-time traffic data at intersections is significant for development of adaptive traffic light control systems. Sensors such as infrared radiation and GPS are not capable of providing detailed traffic information. Compared with these sensors, surveillance cameras have the potential to provide real scenes for traffic analysis. In this research, a You Only Look Once (YOLO)-based algorithm is employed to detect and track vehicles from traffic videos, and a predefined road mask is used to determine traffic flow and turning events in different roads. A Kalman filter is used to estimate and predict vehicle speed and location under the condition of background occlusion. The result shows that the proposed algorithm can identify traffic flow and turning events at a root mean square error (RMSE) of 10. The result shows that a Kalman filter with an intersection of union (IOU)-based tracker performs well at the condition of background occlusion. Also, the proposed algorithm can detect and track vehicles at different optical conditions. Bad weather and night-time will influence the detecting and tracking process in areas far from traffic cameras. The traffic flow extracted from traffic videos contains road information, so it can not only help with single intersection control, but also provides information for a road network. The temporal characteristic of observed traffic flow gives the potential to predict traffic flow based on detected traffic flow, which will make the traffic light control more efficient.