An all-densities pedestrian simulator based on a dynamic evaluation of the interpersonal distances

E. Cristiani, M. Menci, A. Malagnino and G.G. Amaro

Physica A: Statistical Mechanics and its Applications, 2023, vol. 616, issue C

Abstract: In this paper we deal with pedestrian modeling, aiming at simulating crowd behavior in normal and emergency scenarios, including highly congested mass events. We are specifically concerned with a new agent-based, continuous-in-space, discrete-in-time, nondifferential model, where pedestrians have finite size and are compressible to a certain extent. The model also takes into account the pushing behavior appearing at extremely high densities. The main novelty is that pedestrians are not assumed to generate any kind of “field” which governs the dynamics of the others in the space around them. Instead, the behavior of each pedestrian solely relies on its knowledge of the environment and the evaluation of interpersonal distances between it and the others. The model is able to reproduce the concave/concave fundamental diagram with a “double hump” (i.e. with a second peak) which shows up when body forces come into play. We present several numerical tests (some of them being inspired by the recent ISO 20414 standard), which show how the model can reproduce classical self-organizing patterns.

Keywords: Pedestrians models; Crowd models; High densities; Congestion; Pushing behavior; Optimal step models; Velocity models (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:616:y:2023:i:c:s0378437123001802

DOI: 10.1016/j.physa.2023.128625

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