Modeling pedestrian fundamental diagram based on Directional Statistics
<|reference_start|>Modeling pedestrian fundamental diagram based on Directional Statistics: Understanding pedestrian dynamics is crucial for appropriately designing pedestrian spaces. The pedestrian fundamental diagram (FD), which describes the relationship between pedestrian flow and density within a given space, characterizes these dynamics. Pedestrian FDs are significantly influenced by the flow type, such as uni-directional, bi-directional, and crossing flows. However, to the authors' knowledge, generalized pedestrian FDs that are applicable to various flow types have not been proposed. This may be due to the difficulty of using statistical methods to characterize the flow types. The flow types significantly depend on the angles of pedestrian movement; however, these angles cannot be processed by standard statistics due to their periodicity. In this study, we propose a comprehensive model for pedestrian FDs that can describe the pedestrian dynamics for various flow types by applying Directional Statistics. First, we develop a novel statistic describing the pedestrian flow type solely from pedestrian trajectory data using Directional Statistics. Then, we formulate a comprehensive pedestrian FD model that can be applied to various flow types by incorporating the proposed statistics into a traditional pedestrian FD model. The proposed model was validated using actual pedestrian trajectory data. The results confirmed that the model effectively represents the essential nature of pedestrian dynamics, such as the capacity reduction due to conflict of crossing flows and the capacity improvement due to the lane formation in bi-directional flows.<|reference_end|>
@article{nagasaki2024modeling,
    title={Modeling pedestrian fundamental diagram based on Directional Statistics},
    author={Kota Nagasaki, Keiichiro Fujiya, Toru Seo},
    journal={arXiv preprint arXiv:2405.01171},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.01171},
    primaryClass={nlin.AO cs.SY eess.SY physics.soc-ph}
}