Saturation Flow Under Adverse Weather Conditions

When modeling signalized intersections, one parameter is of crucial importance - the saturation flow rate. This value defines the number of vehicles passing an intersection within one hour of effective green time per lane. In this work we investigate changes of the saturation flow under adverse weather conditions like precipitation or snow covered road surface. We obtained data from video recordings and recorded the timestamp for each vehicle passing the intersection with its rear axle. Subsequently, we aggregated all observations 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 are the incorporation of different vehicle classes and the consideration of driving behavior at the beginning and the end of the green phase (start and end lag). We investigated these parameters for different weather situations as well and show that saturation flow rate is significantly influenced by snow on the road surface. Thus it is important to consider weather dependency of this parameter to improve traffic models. We investigated adjustments of the parameters for a microscopic traffic simulation (VISSIM) in order to adjust saturation flow in dependence of prevailing weather conditions.