Probabilistic speed-density relationship for pedestrian traffic: a data-driven approach

We propose a probabilistic modeling approach to represent the speed-density relationship of pedestrian traffic. The approach is data-driven, and it is motivated by the presence of high scatter in the raw data that we have analyzed. We show the validity of the proposed approach, and its superiority compared to deterministic approaches from the literature using a dataset collected from a real scene and another from a controlled experiment.