A novel image-based convolutional neural network approach for traffic congestion estimation

Traditional image-based traffic congestion estimation methods generally include two steps, which first extract the vehicles from the surveillance images, then calculate the congestion index using the vehicle counts. When working with vast amount of video frames, these approaches are time-consuming and hardly guarantee the real time detection of traffic congestion. In this study, firstly a specific and accurate definition of traffic congestion is proposed to quantify the level of traffic congestion. Then we construct an image-based traffic congestion estimation framework, in which a traffic parameter layer is integrated to the basic convolutional neural network (CNN) model. The proposed framework can directly perform traffic congestion calculation and estimation, which shortens the processing time and avoids the complicated postprocessing. A dataset of 1400 traffic images including 66,890 vehicles is collected for training the proposed CNN model. Another new dataset of 2400 traffic images including 113,516 vehicles is collected to test the proposed method on estimating traffic congestion. Experimental results show that our proposed approach has better efficiency and stability in both free flow and congested traffic conditions, as well as sunny and rainy scenes.