Pedestrian simulations in hexagonal cell local field model

Leng, Biao; Wang, Jianyuan; Zhang, Xiong

doi:10.1016/j.physa.2015.07.005

Cited by 5 publications

(1 citation statement)

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“…Each grid has been assigned a potential field value which can reflect the utility cost such as distance, congestion, discomfort cost, and the influence of other pedestrians. [29][30][31][32][33] A gradient can therefore be applied based on the potential field that guides the agents from the source to the destination. These methods are useful to reduce the complexity at a macroscopic level.…”

Section: Introductionmentioning

confidence: 99%

A route navigation algorithm for pedestrian simulation based on grid potential field

Wei

2019

Advances in Mechanical Engineering

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Pedestrian simulation modeling has become an important means to study the dynamic characters of dense populations. In the continuous pedestrian simulation model for complex simulation scenario with obstacles, the pedestrian path planning algorithm is an indispensable component, which is used for the calculation of pedestrian macro path and microscopic movement desired direction. However, there is less efficiency and poor robustness in the existing pedestrian path planning algorithm. To address this issue, we propose a new pedestrian path planning algorithm to solve these problems in this article. In our algorithm, we have two steps to determine pedestrian movement path, that is, the discrete potential fields are first generated by the flood fill algorithm and then the pedestrian desired speeds are determined along the negative gradient direction in the discrete potential field. Combined with the social force model, the proposed algorithm is applied in a corridor, a simple scene, and a complex scene, respectively, to verify its effectiveness and efficiency. The results demonstrate that the proposed pedestrian path planning algorithm in this article can greatly improve the computational efficiency of the continuous pedestrian simulation model, strengthen the robustness of application in complex scenes.

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Section: Introductionmentioning

confidence: 99%