An accurate prediction of actual traffic conditions on freeways is essential for efficient traffic management, safety, and planning. To this end, the knowledge on which traffic state or more exactly which congestion pattern is prevailing, is the crucial basis for any analysis. In this paper, we propose two models, a standard neural network (NN) and a Long Short-Term Memory (LSTM) neural network, for predicting traffic congestion patterns. We provide a concept containing an overview of the problem statement and its significance, discuss the strengths and weaknesses of the proposed models, and outline the data and methodology that will be used for their development. Likewise, we want to compare the results with a simply explainable mixed logit model. As a data base, we use a rich data set of congestion patterns on the A9 freeway in Germany near Munich. Various explanatory variables are inspected to capture the freeway’s layout, previous congestion patterns, speed, and flow information. Our research underscores the significance of incorporating spatio-temporal patterns when predicting traffic conditions on freeways. This approach leads to a significant enhancement in the accuracy of traffic state prediction.      «

An accurate prediction of actual traffic conditions on freeways is essential for efficient traffic management, safety, and planning. To this end, the knowledge on which traffic state or more exactly which congestion pattern is prevailing, is the crucial basis for any analysis. In this paper, we propose two models, a standard neural network (NN) and a Long Short-Term Memory (LSTM) neural network, for predicting traffic congestion patterns. We provide a concept containing an overview of the proble...     »