Artificial neural network based modeling on unidirectional and bidirectional pedestrian flow at straight corridors

Xuedan Zhao, Long Xia, Jun Zhang and Weiguo Song

Physica A: Statistical Mechanics and its Applications, 2020, vol. 547, issue C

Abstract: Pedestrian modeling is a good way to predict pedestrian movement and thus can be used for controlling pedestrian crowds and guiding evacuations in emergencies. In this paper, we propose a pedestrian movement model based on artificial neural network. In the model, the pedestrian velocity vectors are predicted with two sub models, Semicircular Forward Space Based submodel (SFSB-submodel) and Rectangular Forward Space Based submodel (RFSB-submodel), respectively. Both unidirectional and bidirectional pedestrian flow at straight corridors are investigated by comparing the simulation and the corresponding experimental results. It is shown that the pedestrian trajectories and the fundamental diagrams from the model are all consistent with that from experiments. And the typical lane-formation phenomena are observed in bidirectional flow simulation. In addition, to quantitatively evaluate the precision of the prediction, the mean destination error (MDE) and mean trajectory error (MTE) are defined and calculated to be approximately 0.2 m and 0.12 m in unidirectional flow scenario. In bidirectional flow, relative distance error (RDE) is about 0.15 m. The findings indicate that the proposed model is reasonable and capable of simulating the unidirectional and bidirectional pedestrian flow illustrated in this paper.

Keywords: Pedestrian movement modeling; Artificial neural network; Unidirectional flow; Bidirectional flow (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:547:y:2020:i:c:s0378437119321272

DOI: 10.1016/j.physa.2019.123825

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