JDR Vol.19 No.2 pp. 336-346
doi: 10.20965/jdr.2024.p0336


Evaluating Pedestrian Congestion Based on Missing Sensing Data

Xiaolu Jia*,**,† ORCID Icon, Claudio Feliciani*,** ORCID Icon, Sakurako Tanida*,** ORCID Icon, Daichi Yanagisawa*,** ORCID Icon, and Katsuhiro Nishinari*,** ORCID Icon

*Department of Aeronautics and Astronautics, School of Engineering, The University of Tokyo
7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan

**Research Center for Advanced Science and Technology, The University of Tokyo
Tokyo, Japan

Corresponding author

October 8, 2023
November 21, 2023
April 1, 2024
pedestrian congestion, congestion evaluation, missing data, pedestrian density, congestion number

Accurately evaluating pedestrian congestion is crucial for evidence-based improvements in various walking environments. Tracking pedestrian movements in real-world settings often leads to incomplete data collection. Despite this challenge, pedestrian congestion with missing data has not been extensively addressed in existing research. This study examined the impact of missing data on density, speed, and congestion number in the course of evaluating pedestrian congestion. While density is the most commonly used index, speed and congestion number proved more robust.

Cite this article as:
X. Jia, C. Feliciani, S. Tanida, D. Yanagisawa, and K. Nishinari, “Evaluating Pedestrian Congestion Based on Missing Sensing Data,” J. Disaster Res., Vol.19 No.2, pp. 336-346, 2024.
Data files:
  1. [1] C. Feliciani, A. Corbetta, M. Haghani, and K. Nishinari, “Trends in crowd accidents based on an analysis of press reports,” Safety Science, Vol.164, Article No.106174, 2023.
  2. [2] D. Helbing, I. Farkas, and T. Vicsek, “Simulating dynamical features of escape panic,” Nature, Vol.407, No.6803, pp. 487-490, 2000.
  3. [3] C. Burstedde, K. Klauck, A. Schadschneider, and J. Zittartz, “Simulation of pedestrian dynamics using a two-dimensional cellular automaton,” Physica A: Statistical Mechanics and its Applications, Vol.295, Nos.3-4, pp. 507-525, 2001.
  4. [4] D. C. Duives, W. Daamen, and S. P. Hoogendoorn, “State-of-the-art crowd motion simulation models,” Transportation Research Part C: Emerging Technologies, Vol.37, pp. 193-209, 2013.
  5. [5] S. Huang, J. Ji, Y. Wang, W. Li, and Y. Zheng, “A machine vision-based method for crowd density estimation and evacuation simulation,” Safety Science, Vol.167, Article No.106285, 2023.
  6. [6] R. Gade and T. B. Moeslund, “Thermal cameras and applications: A survey,” Machine Vision and Applications, Vol.25, No.1, pp. 245-262, 2014.
  7. [7] B. Huang, G. Mao, Y. Qin, and Y. Wei, “Pedestrian flow estimation through passive WiFi sensing,” IEEE Trans. on Mobile Computing, Vol.20, No.4, pp. 1529-1542, 2021.
  8. [8] Y. Yoshimura, A. Amini, S. Sobolevsky, J. Blat, and C. Ratti, “Analysis of pedestrian behaviors through non-invasive Bluetooth monitoring,” Applied Geography, Vol.81, pp. 43-51, 2017.
  9. [9] A. Angel, A. Cohen, S. Dalyot, and P. Plaut, “Estimating pedestrian traffic with Bluetooth sensor technology,” Geo-Spatial Information Science, 2023.
  10. [10] K. Kidono, T. Miyasaka, A. Watanabe, T. Naito, and J. Miura, “Pedestrian recognition using high-definition LIDAR,” 2011 IEEE Intelligent Vehicles Symp. (IV), pp. 405-410, 2011.
  11. [11] A. Corbetta, J. A. Meeusen, C.-M. Lee, R. Benzi, and F. Toschi, “Physics-based modeling and data representation of pairwise interactions among pedestrians,” Physical Review E, Vol.98, No.6, Article No.062310, 2018.
  12. [12] E. Foxlin, “Pedestrian tracking with shoe-mounted inertial sensors,” IEEE Computer Graphics and Applications, Vol.25, No.6, pp. 38-46, 2005.
  13. [13] T. Kitazato, M. Hoshino, M. Ito, and K. Sezaki, “Detection of pedestrian flow using mobile devices for evacuation guiding in disaster,” J. Disaster Res., Vol.13, No.2, pp. 303-312, 2018.
  14. [14] B. Steffen and A. Seyfried, “Methods for measuring pedestrian density, flow, speed and direction with minimal scatter,” Physica A: Statistical Mechanics and its Applications, Vol.389, No.9, pp. 1902-1910, 2010.
  15. [15] J. Vacková and M. Bukáček, “Kernel estimates as general concept for the measuring of pedestrian density,” Transportmetrica A: Transport Science, 2023.
  16. [16] D. C. Duives, W. Daamen, and S. P. Hoogendoorn, “Quantification of the level of crowdedness for pedestrian movements,” Physica A: Statistical Mechanics and its Applications, Vol.427, pp. 162-180, 2015.
  17. [17] J. J. Fruin, “Pedestrian Planning and Design,” Metropolitan Association of Urban Designers and Environmental Planners, 1971.
  18. [18] Transportation Research Board, National Research of Council, “Highway Capacity Manual,” 2000.
  19. [19] X. Jia, C. Feliciani, H. Murakami, A. Nagahama, D. Yanagisawa, and K. Nishinari, “Revisiting the level-of-service framework for pedestrian comfortability: Velocity depicts more accurate perceived congestion than local density,” Transportation Research Part F: Traffic Psychology and Behaviour, Vol.87, pp. 403-425, 2022.
  20. [20] C. Feliciani and K. Nishinari, “Measurement of congestion and intrinsic risk in pedestrian crowds,” Transportation Research Part C: Emerging Technologies, Vol.91, pp. 124-155, 2018.
  21. [21] F. Zanlungo, C. Feliciani, Z. Yücel, X. Jia, K. Nishinari, and T. Kanda, “A pure number to assess ‘congestion’ in pedestrian crowds,” Transportation Research Part C: Emerging Technologies, Vol.148, Article No.104041, 2023.
  22. [22] J. Du, M. Hu, and W. Zhang, “Missing data problem in the monitoring system: A review,” IEEE Sensors J., Vol.20, No.23, pp. 13984-13998, 2020.
  23. [23] Guinness World Records, “Busiest station,” 2018.
  24. [24] East Japan Railway Company, “Major station information–Shinjuku,” 2023.
  25. [25] PTV Planung Transport Verkehr GmbH, “Pedestrian simulation software Viswalk,” 2023.
  26. [26] J. E. Anderson, “The gravity model,” Annual Review of Economics, Vol.3, No.1, pp. 133-160, 2011.
  27. [27] Y. Ahn, T. Kowada, H. Tsukaguchi, and U. Vandebona, “Estimation of passenger flow for planning and management of railway stations,” Transportation Research Procedia, Vol.25, pp. 315-330, 2017.
  28. [28] D. Helbing, L. Buzna, A. Johansson, and T. Werner, “Self-organized pedestrian crowd dynamics: Experiments, simulations, and design solutions,” Transportation Science, Vol.39, No.1, pp. 1-24, 2005.
  29. [29] X. Jia, H. Murakami, C. Feliciani, D. Yanagisawa, and K. Nishinari, “Pedestrian lane formation and its influence on egress efficiency in the presence of an obstacle,” Safety Science, Vol.144, Article No.105455, 2021.
  30. [30] C. Feliciani and K. Nishinari, “Empirical analysis of the lane formation process in bidirectional pedestrian flow,” Physical Review E, Vol.94, No.3, Article No.032304, 2016.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Apr. 05, 2024