JDR Vol.19 No.2 pp. 325-335
doi: 10.20965/jdr.2024.p0325


Influencing Pedestrian Route Choice Through Environmental Stimuli: A Long-Term Ecological Experiment

Claudio Feliciani*,**,† ORCID Icon, Sakurako Tanida*,** ORCID Icon, Xiaolu Jia*,** 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 4, 2023
January 9, 2024
April 1, 2024
crowd, environmental stimuli, route choice, pedestrian, crowd control

Urban centers are getting crowded, public transportation is becoming congested, and mass events are attracting an increasing number of people. Crowd disasters are not rare, and to prevent them the careful planning of pedestrian facilities and collaboration among stakeholders in the organization of events are crucial. When communication and coordination among stakeholders are sufficient, safety can usually be achieved; however, even in such cases, unexpected situations may occur. Automated crowd-control methods are required to address such situations. However, little is known about how crowd behavior can be influenced without direct human intervention. In this study, we investigated the use of environmental stimuli to modify pedestrian behavior (more specifically, route choice) in an educational facility. Colors, lights, signs, and sounds were used to influence route selection. The results show that light and, in part, LED information displays are somehow effective and could be valid candidates to pave the way for automated crowd control systems (especially for night events). The experiment presented here considers low crowd density. However, we believe that this could help encourage the balanced use of space by pedestrians under normal conditions and establish good practices. In turn, this can delay the creation of high densities, which are often the cause of fatalities in crowd disasters, and provide staff with time for intervention.

Cite this article as:
C. Feliciani, S. Tanida, X. Jia, and K. Nishinari, “Influencing Pedestrian Route Choice Through Environmental Stimuli: A Long-Term Ecological Experiment,” J. Disaster Res., Vol.19 No.2, pp. 325-335, 2024.
Data files:
  1. [1] N. Sekiya, “Panic and Crowd Disaster in Underground Space,” J. Disaster Res., Vol.11, No.2, pp. 306-314, 2016.
  2. [2] 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.
  3. [3] C. Feliciani, K. Shimura, and K. Nishinari, “Introduction to Crowd Management: Managing Crowds in the Digital Era: Theory and Practice,” Springer Nature, 2022.
  4. [4] M. Haghani, M. Coughlan, B. Crabb, A. Dierickx, C. Feliciani, R. van Gelder, P. Geoerg, N. Hocaoglu, S. Laws, R. Lovreglio, Z. Miles, A. Nicolas, W. J. O’Toole, S. Schaap, T. Semmens, Z. Shahhoseini, R. Spaaij, A. Tatrai, J. Webster, and A. Wilson, “A Roadmap for the Future of Crowd Safety Research and Practice: Introducing the Swiss Cheese Model of Crowd Safety and the Imperative of a Vision Zero Target,” Safety Science, Vol.168, Article No.106292, 2023.
  5. [5] A. Brunetti, D. Buongiorno, G. F. Trotta, and V. Bevilacqua, “Computer Vision and Deep Learning Techniques for Pedestrian Detection and Tracking: A Survey,” Neurocomputing, Vol.300, pp. 17-33, 2018.
  6. [6] A. Tatrai, “How Do We Solve Wicked Problems? Effective Crowd Management,” J. of Behavioural Economics and Social Systems, Vol.3, No.1, pp. 52-65, 2021.
  7. [7] K. Kidono, T. Miyasaka, A. Watanabe, T. Naito, and J. Miura, “Pedestrian Recognition Using High-Definition LIDAR,” 2011 IEEE Intelligent Vehicles Symposium (IV), pp. 405-410, 2011.
  8. [8] D. Brščić, T. Kanda, T. Ikeda, and T. Miyashita, “Person Tracking in Large Public Spaces Using 3-D Range Sensors,” IEEE Trans. on Human-Machine Systems, Vol.43, No.6, pp. 522-534, 2013.
  9. [9] 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.
  10. [10] A. Danalet, B. Farooq, and M. Bierlaire, “A Bayesian Approach to Detect Pedestrian Destination-Sequences from WiFi Signatures,” Transportation Research Part C: Emerging Technologies, Vol.44, pp. 146-170, 2014.
  11. [11] 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.
  12. [12] G. Proulx and J. D. Sime, “To Prevent ‘Panic’ in an Underground Emergency: Why Not Tell People the Truth,” Fire Safety Science, Vol.3, pp. 843-852, 1991.
  13. [13] M. Lombardi, “Communication in Emergencies,” Vita e Pensiero, 2005 (in Italian).
  14. [14] H. Li, M. Li, H. Zou, Y. Zhang, and J. Cao, “Urban Sensory Map: How Do Tourists “Sense” a Destination Spatially?,” Tourism Management, Vol.97, Article No.104723, 2023.
  15. [15] M. Ba, J. Kang, and Z. Li, “The Effects of Sounds and Food Odour on Crowd Behaviours in Urban Public Open Spaces,” Building and Environment, Vol.182, Article No.107104, 2020.
  16. [16] D. Szakál, O. Fehér, D. Radványi, and A. Gere, “Effect of Scents on Gazing Behavior and Choice,” Applied Sciences, Vol.12, No.14, Article No.6899, 2022.
  17. [17] D. Gibson, “The Wayfinding Handbook: Information Design for Public Places,” Princeton Architectural Press, 2009.
  18. [18] S. A. Eroglu, K. A. Machleit, and J.-C. Chebat, “The Interaction of Retail Density and Music Tempo: Effects on Shopper Responses,” Psychology & Marketing, Vol.22, No.7, pp. 577-589, 2005.
  19. [19] N. Kuratomo, H. Miyakawa, S. Masuko, T. Yamanaka, and K. Zempo, “Effects of Acoustic Comfort and Advertisement Recallability on Digital Signage with On-Demand Pinpoint Audio System,” Applied Acoustics, Vol.184, Article No.108359, 2021.
  20. [20] M. Costa, S. Frumento, M. Nese, and I. Predieri, “Interior Color and Psychological Functioning in a University Residence Hall,” Frontiers in Psychology, Vol.9, Article No.1580, 2018.
  21. [21] D. Yanagisawa, A. Tomoeda, and K. Nishinari, “Improvement of Pedestrian Flow by Slow Rhythm,” Physical Review E, Vol.85, No.1, Article No.016111, 2012.
  22. [22] G. Zeng, A. Schadschneider, J. Zhang, S. Wei, W. Song, and R. Ba, “Experimental Study on the Effect of Background Music on Pedestrian Movement at High Density,” Physics Letters A, Vol.383, No.10, pp. 1011-1018, 2019.
  23. [23] Q. Meng, T. Zhao, and J. Kang, “Influence of Music on the Behaviors of Crowd in Urban Open Public Spaces,” Frontiers in Psychology, Vol.9, Article No.596, 2018.
  24. [24] T. Senan, A. Corbetta, and B. Hengeveld, “Towards Sound-based Crowd Management: Investigating Sonification for Pedestrian Steering,” Proc. of the 17th Int. Audio Mostly Conf., pp. 32-35, 2022.
  25. [25] N. J. Buikstra, “Off the Beaten Track: The Effectiveness of Illuminance Difference-Based Nudges on Route Choices of Pedestrians in a Crowd,” Master’s thesis, Eindhoven University of Technology, 2021.
  26. [26] D. Ingi, P. Bhusal, P. Pinho, M. Kyttä, and M. Parker, “Ways to Study Changes in Pedestrians’ Behaviour in the Artificially Lit Urban Outdoor Environment,” IOP Conf. Series: Earth and Environmental Science, Vol.1099, Article No.012007, 2022.
  27. [27] A. Corbetta, W. Kroneman, M. Donners, A. Haans, P. Ross, M. Trouwborst, S. Van de Wijdeven, M. Hultermans, D. Sekulovski, F. van der Heijden, S. Mentink, and F. Toschi, “A Large-Scale Real-Life Crowd Steering Experiment via Arrow-Like Stimuli,” Pedestrian and Evacuation Dynamics, Vol.5, pp. 61-68, 2018.
  28. [28] H. Murakami, C. Feliciani, K. Shimura, and K. Nishinari, “A System for Efficient Egress Scheduling During Mass Events and Small-Scale Experimental Demonstration,” Royal Society Open Science, Vol.7, No.12, Article No.201465, 2020.
  29. [29] Japan Meteorological Agency, “Past Weather Data.” [Accessed October 1, 2023]
  30. [30] National Astronomical Observatory of Japan, “Sunrise and Sunlight Times.” [Accessed October 1, 2023]
  31. [31] C. Feliciani, K. Shimura, D. Yanagisawa, and K. Nishinari, “Study on the Efficacy of Crowd Control and Information Provision Through a Simple Cellular Automata Model,” Int. Conf. on Cellular Automata, pp. 470-480, 2018.
  32. [32] C. Feliciani, H. Murakami, K. Shimura, and K. Nishinari, “Efficiently Informing Crowds-Experiments and Simulations on Route Choice and Decision Making in Pedestrian Crowds with Wheelchair Users,” Transportation Research Part C: Emerging Technologies, Vol.114, pp. 484-503, 2020.
  33. [33] H. Murakami, T. Tomaru, C. Feliciani, and Y. Nishiyama, “Spontaneous Behavioral Coordination Between Avoiding Pedestrians Requires Mutual Anticipation Rather Than Mutual Gaze,” iScience, Vol.25, No.11, Article No.105474, 2022.
  34. [34] A. Nagahama, K. Tanaka, C. Feliciani, G. Cui, and T. Wada, “Effects of Urban Landscape and Soundscape on Driving Behavior,” 2022 IEEE Conf. on Cognitive and Computational Aspects of Situation Management (CogSIMA), pp. 84-88, 2022.
  35. [35] A. López, F. Chaumette, E. Marchand, and J. Pettré, “Attracted by Light: Vision-Based Steering Virtual Characters Among Dark and Light Obstacles,” Proc. of the 12th ACM SIGGRAPH Conf. on Motion, Interaction and Games, pp. 1-6, 2019.

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