Saturation Flow Under Adverse Weather Conditions

In the modeling of signalized intersections, one parameter is of crucial importance: the saturation flow rate. This value defines the number of vehicles that pass an intersection within 1 h of effective green time per lane. In this study, changes in the saturation flow were investigated under adverse weather conditions, such as precipitation or snow that covered the road surface. Data were obtained from video recordings, and a timestamp was recorded for each vehicle as its rear axle crossed the intersection. Subsequently, all observations were aggregated to a longer time interval. These measurements were then used to train a model by minimizing the squared error between model output and observation. The advantages of the model were the incorporation of various vehicle classes and the consideration of driving behavior at the beginning and the end of the green phase (start and end lags). These parameters were investigated under various weather conditions and showed that the saturation flow rate was significantly influenced by snow on the road surface. To improve traffic models, it is thus important to consider the dependence of the saturation flow rate on the weather. To adjust the saturation flow, adjustments in certain other parameters influenced by prevailing weather conditions were investigated in a microscopic traffic simulation.