Probabilistic speed–density relationship for pedestrian traffic. (July 2016) - Nikolić, Marija; Bierlaire, Michel; Farooq, Bilal; de Lapparent, Matthieu
Highlights: Probabilistic modeling approach to characterize speed–density relationship of pedestrian traffic. Data-driven approach which is motivated by the empirically observed heterogeneity. Model estimation and validation based on two case studies (data from a real scene and from controlled experiments). Results show satisfactory predictive capabilities of the model and its superiority compared to the deterministic approaches from the literature. Abstract: 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.