A macroscopic loading model for multi-directional, time-varying and congested pedestrian flows is proposed in this paper. Walkable space is represented by a network of streams that are each associated with an area in which they interact. To describe this interaction, a stream-based pedestrian fundamental diagram is used that relates density and walking speed in multi-directional flow. The proposed model is applied to two different case studies. The explicit modeling of anisotropy in walking speed is shown to significantly improve the ability of the model to reproduce empirically observed walking time distributions. Moreover, the obtained model parametrization is in excellent agreement with the literature.

We propose a probabilistic modeling approach to represent the speed-density relationship of pedestrian trac. 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.

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We introduce a modeling approach for pedestrian speed-density relationship. It is motivated by a high scatter in real data that precludes the use of traditional equilibrium relationships. To characterize the observed pattern we relax the homogeneity assumption of equilibrium relations and propose a multi-class model. In addition to the general modeling framework, we also present some concrete model specifications. Real data is utilized to test the performance of the approach. The approach is able to reveal fundamental properties causing the heterogeneity in population and describe their impact on pedestrian movement. We also show the advantages of the proposed approach compared to approaches from the literature. The proposed model is flexible, and it provides better fit and richer information than traditional models.

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