Chaotic Analysis of Pedestrian Flow Series for Vehicular Networking in Complex Interaction Environment
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所属单位：School of Computer Science and Information Engineering, Hefei University of Technology
发表刊物：2023 9th International Conference on Computer and Communications
项目来源：National Natural Science Foundation of China with Grants 62371180, 61971176 and 62171474，National Ke
关键字：vehicular networking, pedestrian flow time series, chaos theory, phase space reconstruction, characteristic analysis
摘要：Interaction between self-driving cars and pedestrians is a key issue for the design of vehicular networking and auto-drive algorithms. Pedestrian movement usually exhibits irregular and complex behaviors, dramatically increasing the difficulty of such design. By choosing a suitable method to analyze the pedestrian flow and obtain its major features, self-driving cars and service robots could intelligently avoid collision with pedestrians. Therefore, this paper analyzes the chaotic characteristics of the pedestrian flow time series of different locations in real campus scenes qualitatively and quantitatively. The optimal delay and optimal embedding dimension of pedestrian flow time series are obtained by the mutual information method and Cao’s method, respectively. The largest Lyapunov exponent, correlation dimension, and Kolmogorov entropy are used to quantitatively analyze its chaos, and recurrence plot is used for qualitative demonstrations. According to this study, pedestrian flow time series are definitely chaotic, which provides essential theoretical support for pedestrian flow prediction and auto-drive algorithm design in future vehicular networking.
合写作者：Lusheng Wang,Caihong Kai,Kongjin Zhu
第一作者：Siqi Qi
论文类型：论文集
学科门类：工学
文献类型：C
页面范围：1322-1328
是否译文：否
发表时间：2023-12-08
收录刊物：EI