Traffic jam detection and density estimation of aerial images have been widely utilized in various scenarios, such as vehicle routing and city management. Rather than directly detecting traffic jams or estimating density, traffic condition analysis based on traffic jam segmentation could yield more accurate results. Therefore, we propose a Context Enhanced Traffic Segmentation Model to simultaneously segment the traffic jam parts and road surface. However, there are two critical issues for traffic jam segmentation in aerial images: one is the scale variation problem and the other is the difficulty of accurately segmenting ambiguous traffic jam boundaries. Thus, we design a traffic estimation module to handle the scale variation problem and present a context attention module to enhance the boundary of traffic jam segmentation. Experimental results demonstrate the superiority of our proposed method.