JDR Vol.13 No.2 pp. 303-312
doi: 10.20965/jdr.2018.p0303


Detection of Pedestrian Flow Using Mobile Devices for Evacuation Guiding in Disaster

Tomoya Kitazato*, Miku Hoshino*, Masaki Ito*, and Kaoru Sezaki*,†

*Institute of Industrial Science, the University of Tokyo
4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan

**Center for Spatial Information Science, the University of Tokyo, Chiba, Japan

Corresponding author

October 27, 2017
January 24, 2018
Online released:
March 19, 2018
March 20, 2018
crowd sensing, evacuation guidance, mobile sensing.

In March 2011, the Great East Japan Earthquake occurred, killing approximately 20,000 people. Previous research has shown that evacuation start time and evacuation behavior are related to the disaster survival rate: immediate evacuation increases the survival rate and evacuation-disruption caused by traffic congestion decreases it. Therefore, it can be assumed that guiding people to safe locations will increase the survival rate. The detection of the human mobility flow is a key to rescuing more people, because its analysis can help determine the appropriate evacuation routes toward which people should be guided. The objective of our research is to develop a system for detecting the human mobility flows in a disaster scenario. We analyzed the requirements of human mobility flow detection for disaster evacuation guidance. In this paper, we propose a crowd sensing system that uses Bluetooth for recognizing human mobility flows. By detecting Bluetooth devices carried by pedestrians, the congestion degree can be estimated. Further, the devices’ movements can be detected by observing the received signal strength indicator (RSSI) of Bluetooth Low Energy (LE) beacons carried by pedestrians. The results of experimental evaluations of these two methods verify their usefulness. Our methods can estimate the congestion degree, as well as the velocity of walking pedestrians.

Cite this article as:
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.
Data files:
  1. [1] N. Y. Yun and M. Hamada, “Evacuation Behaviors in the 2011 Great East Japan Earthquake,” Journal of Disaster Research, Vol.7, No.sp, pp. 458–467, 2012.
  2. [2] L. Hong, Y. Zheng, D. Yung, J. Shang, and L. Zou, “Detecting Urban Black Holes Based on Human Mobility Data,” In Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 35:1–35:10, November 2015, doi:10.1145/2820783.2820811.
  3. [3] A. V. Khezerlou, X. Zhou, L. Li, Z. Shafiq, A. X. Liu, and F. Zhang, “A Traffic Flow Approach to Early Detection of Gathering Events: Comprehensive Results,” ACM Transactions on Intelligent Systems and Technology, Vol.8, No.6, pp. 74:1–74:24, 2017, doi:10.1145/3078850.
  4. [4] S. Koshimura, “Establishing the Advanced Disaster Reduction Management System by Fusion of Real-Time Disaster Simulation and Big Data Assimilation,” Journal of Disaster Research, Vol.11, No.2, pp. 164–174, 2016.
  5. [5] T. Osaragi, T. Morisawa, and T. Oki, “Simulation Model of Evacuation Behavior Following a Large-Scale Earthquake that Takes into Account Various Attributes of Residents and Transient Occupants,” In Pedestrian and Evacuation Dynamics 2012, pp. 469–484, June 2012, doi:10.1007/978-3-319-02447-9_39.
  6. [6] Y. Yoshimura, S. Sobolevsky, C. Ratti, F. Girardin, J. P. Carrascal, J. Blat, and R. Sinatra, “An Analysis of Visitors’ Behavior in the Louvre Museum: A Study Using Bluetooth Data,” Environment and Planning B: Planning and Design, Vol.41, No.6, pp. 1113–1131, 2014, doi:10.1068/b130047p.
  7. [7] M. Kotaru, K. Joshi, D. Bharadia, and S. Katti, “SpotFi: Decimeter Level Localization Using WiFi,” In Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication, pp. 269–282, August 2015, doi:10.1145/2829988.2787487.
  8. [8] D. Vasisht, S. Kumar, and D. Katabi, “Decimeter-level Localization with a Single WiFi Access Point,” In Proceedings of the 13th Usenix Conference on Networked Systems Design and Implementation, pp. 165–178, March 2016.
  9. [9] R. Faragher and R. Harle, “An Analysis of the Accuracy of Bluetooth Low Energy for Indoor Positioning Applications,” In Proceedings of the 27th International Technical Meeting of The Satellite Division of the Institute of Navigation, pp. 201–210, The Institute of Navigation, 2014.
  10. [10] J. Wang and D. Katabi, “Dude, Where’s My Card?: RFID Positioning That Works with Multipath and Non-line of Sight,” In Proceedings of the 2013 ACM Conference on Special Interest Group on Data Communication, pp. 51–62, August 2013, doi:10.1145/2486001.2486029.
  11. [11] H. Zhao and R. Shibasaki, “A Novel System for Tracking Pedestrians Using Multiple Single-Row Laser-Range Scanners,” IEEE Transactions on Systems, Man, and Cybernetics – Part A: Systems and Humans, Vol.35, No.2, pp. 283–291, 2005, doi:10.1109/TSMCA.2005.843396.
  12. [12] A. Asahara, N. Sato, M. Nomiya, and S. Tsuji, “LiDAR-based Pedestrian-flow Analysis for Crowdedness Equalization,” In Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 30:1–30:10, November 2015, doi:10.1145/2820783.2820805.
  13. [13] T. Nishimura, T. Higuchi, H. Yamaguchi, and T. Higashino, “Detecting Smoothness of Pedestrian Flows by Participatory Sensing with Mobile Phones,” In Proceedings of the 2014 ACM International Symposium on Wearable Computers, pp. 15–18, September 2014, doi:10.1145/2634317.2642869.
  14. [14] J. Weppner and P. Lukowicz, “Collaborative crowd density estimation with mobile phones,” In 2nd International Workshop on Sensing Applications on Mobile Phones, November 2011.
  15. [15] J. Weppner and P. Lukowicz, “Bluetooth Based Collaborative Crowd Density Estimation with Mobile Phones,” In 2013 IEEE International Conference on Pervasive Computing and Communications, pp. 193–200, March 2013, doi:10.1109/PerCom.2013.6526732.
  16. [16] Bluetooth SIG, Inc., “Bluetooth core specification version 5.0,” 2016.
  17. [17] M. Hoshino, M. Ito, and K. Sezaki, “Pedestrian Flow Detection Using Bluetooth for Evacuation Route Finding,” In Proceedings of the 5th ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems, pp. 84–87, October 2016, doi:10.1145/3004725.3004726.
  18. [18] T. Kitazato, K. Ito, K. Umezawa, M. Ito, and K. Sezaki, “Real-time Visualization of the Degree of Indoor Congestion with Smartphone-based Participatory,” In The 19th International Conference on Human-Computer Interaction, pp. 286–301, July 2017, doi:10.1007/978-3-319-58697-7_21.

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

Last updated on Jun. 19, 2024