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Estimation of crowd density applying wavelet transform and machine learning

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  • Nagao, Koki
  • Yanagisawa, Daichi
  • Nishinari, Katsuhiro

Abstract

We conducted a simple experiment in which one pedestrian passed through a crowded area and measured the body-rotational angular velocity with commercial tablets. Then, we developed a new method for predicting crowd density by applying the continuous wavelet transform and machine learning to the data obtained in the experiment. We found that the accuracy of prediction using angular velocity data was as high as that using raw velocity data. Therefore, we concluded that angular velocity has relationship with crowd density and we could estimate crowd density by angular velocity. Our research will contribute to management of safety and comfort of pedestrians by developing an easy way to measure crowd density.

Suggested Citation

  • Nagao, Koki & Yanagisawa, Daichi & Nishinari, Katsuhiro, 2018. "Estimation of crowd density applying wavelet transform and machine learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 145-163.
    • Handle: RePEc:eee:phsmap:v:510:y:2018:i:c:p:145-163
    DOI: 10.1016/j.physa.2018.06.078
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    References listed on IDEAS

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    1. Zohreh Asadi-Shekari & Mehdi Moeinaddini & Muhammad Zaly Shah, 2013. "Non-motorised Level of Service: Addressing Challenges in Pedestrian and Bicycle Level of Service," Transport Reviews, Taylor & Francis Journals, vol. 33(2), pages 166-194, March.
    2. Ankit Gupta & Nitin Pundir, 2015. "Pedestrian Flow Characteristics Studies: A Review," Transport Reviews, Taylor & Francis Journals, vol. 35(4), pages 445-465, July.
    3. Steffen, B. & Seyfried, A., 2010. "Methods for measuring pedestrian density, flow, speed and direction with minimal scatter," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(9), pages 1902-1910.
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    Cited by:

    1. Haghani, Milad & Sarvi, Majid & Shahhoseini, Zahra, 2019. "When ‘push’ does not come to ‘shove’: Revisiting ‘faster is slower’ in collective egress of human crowds," Transportation Research Part A: Policy and Practice, Elsevier, vol. 122(C), pages 51-69.
    2. Yamamoto, Hiroki & Yanagisawa, Daichi & Feliciani, Claudio & Nishinari, Katsuhiro, 2019. "Body-rotation behavior of pedestrians for collision avoidance in passing and cross flow," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 486-510.
    3. Wang, Shujia & Yang, Zhuojin & Zheng, Xin & Ma, Zhuang, 2024. "Analysis of complex time series based on EEMD energy entropy plane," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).
    4. Xing, Jieli & Zhang, Yongjie & Chu, Gang & Pan, Qi & Zhang, Xiaotao, 2021. "Detection and reconstruction of catastrophic breaks of high-frequency financial data with local linear scaling approximation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 561(C).

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