Traffic jams occur frequently and cause psychological, economic and environmental issues. Hence, understanding traffic congestion is important for both travelers and city planners. While dedicated sensors to detect road usage can provide accurate measurements for selected roads, they are expensive to set up and maintain. On the other hand, public traffic surveillance cameras are ubiquitous and can often be used to detect the current traffic flow and to allow the cities to improve traffic conditions. Many computer vision systems have to be trained for this purpose on large datasets. To support the development and evaluation of such systems, we introduce a new large-scale traffic surveillance video dataset. Recorded over a period of two months, the dataset comprises more than 17000 videos captured from 150 publicly available traffic cameras. It exhibits substantial diversity in both technical properties, e.g. resolution, frame rate, and compression artifacts, and visual characteristics, including camera perspectives and time-of-day variations. Unlike datasets collected in controlled settings, our dataset is captured using real-world traffic surveillance infrastructure, making it a realistic and challenging benchmark for advancing computer vision methods in traffic analysis. The dataset is available at https://www.nes.uni-due.de/research/data/