Analysis of Pedestrian Stress Level Using GSR Sensor in Virtual Immersive Reality


  • Mahwish Mudassar Laboratory of Innovations in Transportation (LiTrans), Ryerson University, Toronto, Canada
  • Arash Kalatian Institute of Transport Studies, University of Leeds, Leeds, United Kingdom
  • Bilal Farooq Laboratory of Innovations in Transportation (LiTrans), Ryerson University, Toronto, Canada



pedestrian crossing behaviour, stress level, GSR, virtual reality, autonomous vehicle


Level of emotional arousal of one's body changes in response to external stimuli in an environment. Given the risks involved while crossing streets, particularly at unsignalized mid-block crosswalks, one can expect a change in the stress level of pedestrians. In this study, we investigate the levels and changes in pedestrian stress, under different road crossing scenarios in immersive virtual reality. To measure stress level of pedestrians, we used Galvanic Skin Response (GSR) sensors. To collect the required data for the model, Virtual Immersive Reality Environment (VIRE) tool is used, which enables us to measure participant's stress levels in a controlled environment. Detailed experiments were conducted over a 5-month period, with 180 participants from four different places in Toronto to cover a heterogeneous population. Data collected are used to develop behavioural models, to observe the contribution of different variables on increasing pedestrian stress level. The initial modelling results suggested that the density of vehicles has a positive effect, meaning as the density of vehicles increases, so does the stress levels for pedestrians. The sociodemographic information has a relationship to individual’s stress levels. It was noted that younger pedestrians have lower amount of stress when crossing as compared to older pedestrians which have higher amounts of stress. Geometric variables has an impact on the stress level of pedestrians. The greater the number of lanes the greater the observed stress, which is due the crossing distance increasing, while the walking speed remaining the same.


Khayyam, H., Javadi, B., Jalili, M., Jazar, R.N.: Artificial intelligence and internet of things for autonomous vehicles. In: Nonlinear approaches in engineering applications,23pp. 39–68. Springer (2020)

Meir, A., Oron-Gilad, T., Parmet, Y.: Are child-pedestrians able to identify hazardous traffic situations? measuring their abilities in a virtual reality environment. Safety science2680, 33–40 (2015)

Quy, R.J., Kubiak, E.W.: A comparison between “aware” and “naive” conditions in the suppression of gsr activity. Quarterly Journal of Experimental Psychology26(4), 561–29565 (1974)

Feliciani, C., Murakami, H., Shimura, K., Nishinari, K.: Efficiently informing crowds–31experiments and simulations on route choice and decision making in pedestrian crowds with wheelchair users. Transportation research part C: emerging technologies114, 484–33503 (2020)

Bendak, S., Alnaqbi, A.M., Alzarooni, M.Y., Aljanaahi, S.M., Alsuwaidi, S.J.: Factors affecting pedestrian behaviors at signalized crosswalks: An empirical study. Journal of safety research (2021)

Shaaban, K., Abdel-Warith, K.: Agent-based modeling of pedestrian behavior at an un-1marked midblock crossing. Procedia Computer Science109, 26–33 (2017)

Brosseau, M., Zangenehpour, S., Saunier, N., Miranda-Moreno, L.: The impact of waiting time and other factors on dangerous pedestrian crossings and violations at signalized intersections: A case study in montreal. Transportation research part F: traffic psychology and behaviour21, 159–172 (2013)

Faria, J.J., Krause, S., Krause, J.: Collective behavior in road crossing pedestrians: the role of social information. Behavioral ecology21(6), 1236–1242 (2010)

Mavros, P., Austwick, M.Z., Smith, A.H.: Geo-eeg: towards the use of eeg in the study of urban behaviour. Applied Spatial Analysis and Policy9(2), 191–212 (2016)

Haghani, M., Sarvi, M.: Human exit choice in crowded built environments: Investigating underlying behavioural differences between normal egress and emergency evacuations. Fire Safety Journal85, 1–9 (2016)

Li, X., Guo, F., Kuang, H., Zhou, H.: Effect of psychological tension on pedestrian counter flow via an extended cost potential field cellular automaton model. Physica A:Statistical Mechanics and its Applications487, 47–57 (2017)

Osaragi, T.: Modeling of pedestrian behavior and its applications to spatial evaluation In: Autonomous Agents and Multiagent Systems, International Joint Conference on,18vol. 3, pp. 836–843. IEEE Computer Society (2004)

Kadali, B.R., Perumal, V.: Pedestrians’ gap acceptance behavior at mid block location.International Journal of Engineering and Technology4(2), 158 (2012)

Holland, C., Hill, R.: The effect of age, gender and driver status on pedestrians’ in-22tentions to cross the road in risky situations. Accident Analysis & Prevention39(2),23224–237 (2007)24

Li, P., Bian, Y., Rong, J., Zhao, L., Shu, S.: Pedestrian crossing behavior at unsignalized mid-block crosswalks around the primary school. Procediasocial and behavioral sciences96, 442–450 (2013)

Zeedyk, M.S., Kelly, L.: Behavioural observations of adult–child pairs at pedestrian crossings. Accident Analysis & Prevention35(5), 771–776 (2003)

Oxley, J.A., Ihsen, E., Fildes, B.N., Charlton, J.L., Day, R.H.: Crossing roads safely: an experimental study of age differences in gap selection by pedestrians. Accident Analysis & Prevention37(5), 962–971 (2005)

Rad, S.R., de Almeida Correia, G.H., Hagenzieker, M.: Pedestrians’ road crossing behaviour in front of automated vehicles: Results from a pedestrian simulation experiment using agent-based modelling. Transportation research part F: traffic psychology and behaviour69, 101–119 (2020)36

Analysis of pedestrian stress level using GSR sensor in virtual immersive reality

Cloutier, M.S., Lachapelle, U., d’Amours Ouellet, A.A., Bergeron, J., Lord, S., Torres,1J.: “outta my way!” individual and environmental correlates of interactions between pedestrians and vehicles during street crossings. Accident Analysis & Prevention104,336–45 (2017)4

Ackermann, C., Beggiato, M., Schubert, S., Krems, J.F.: An experimental study to in-5vestigate design and assessment criteria: What is important for communication between pedestrians and automated vehicles? Applied ergonomics75, 272–282 (2019)

Farooq, B., Cherchi, E., Sobhani, A.: Virtual immersive reality for stated preference8travel behavior experiments: A case study of autonomous vehicles on urban roads. Transportation research record2672(50), 35–45 (2018)




How to Cite

Mudassar, M., Kalatian, A., & Farooq, B. (2022). Analysis of Pedestrian Stress Level Using GSR Sensor in Virtual Immersive Reality. Collective Dynamics, 6, 1–24.



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