Statistical Density Prediction in Traffic Networks

Recently, modern tracking methods started to allow capturing the position of massive numbers of moving objects. Given this information, it is possible to analyze and predict the traffic density in a network which offers valuable information for traffic control, congestion prediction and prevention. In this paper, we propose a novel statistical approach to predict the density on any edge in such a network at a future point of time. Our method is based on short-time observations of the traffic history. Therefore, it is not required to know the destination of each object. Instead, we assume that each object acts rationally and chooses the shortest path from its starting point to its destination. This assumption is employed in a statistical approach describing the likelihood of any given object to be located at some position at a particular point of time. Furthermore, we propose an efficient method to speed up the prediction which is based on a suffix-tree. In our experiments, we show the capability of our approach to make useful predictions about the traffic density and illustrate the efficiency of our new algorithm when calculating these predictions.