International Choice Modelling Conference, International Choice Modelling Conference 2017

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Investigating the effects of stress on choices - Evidence from gap acceptance decisions of drivers in a simulator experiment
Evangelos Paschalidis, Charisma Choudhury, Stephane Hess

Last modified: 28 March 2017

Abstract


The process of choice making is not always rational and consistent to an outside observer, as would be assumed in a traditional choice model. The decisions of the same person can vary depending on the context as well as state of mind of the decision maker (e.g. whether they are stressed, fatigued, excited, etc.). In particular, previous researches have shown that variation in mood significantly affects the risk taking behaviour of individuals [‎1,2] and the rationality behind decision making, often resulting in impulsive behaviour and irrational decisions [‎3].

State-of-the art choice models typically rely on the observed characteristics of the decision maker along with attitudinal questions to capture the heterogeneity in the decisions made by different individuals. The use of continuous measurement of mood, which has been used in the context of activity and travel choice models, is typically limited to retrospective methods (e.g. The Day Reconstruction Method [‎4] and Experience Sampling Method [‎5]). In a parallel stream of research, brain activity measurement technologies like Functional Magnetic Resonance Imaging (fMRI) have been used in the emerging field of Neuroeconomics to get insights about the choice making process of individuals [‎6,‎7]. These methods are still in their infancy though and because of their intrusive nature, non-applicable outside the laboratory conditions. However, the findings in this field reinforce the notion that physiological and emotional states have significant effects on the choice making process. Furthermore, the effect of brain activity measurements is yet to be integrated in the mathematical models of choices.

This paper aims to fill in this research gap by developing a hybrid choice model framework for integrating the effect of stress on choice models and applies the framework to investigate the effect of stress on driving behaviour. Traditional driving behaviour models are estimated based on traffic or simulated data, thus, they ignore the heterogeneity in drivers’ attributes [‎8‎,9]. However, there is evidence in the literature [10] that drivers’ behaviour depends not only on traffic related factors but also on their characteristics (e.g. sociodemographic, personality), their emotional state (stress in particular) and trip related factors (e.g. time urgency). Previous research based on self-reported stress levels has shown that stressed drivers are occupied by negative feelings and are more likely to get involved in hazardous situations, than those who are relaxed [11]. Although state-of-the art research on driving safety acknowledges the impact of stress on safety, to the best of our knowledge this is the first attempt to develop mathematical models of drivers’ choices which quantify the impact of stress on driving decisions.

The models developed in this research are based on an extensive experimental study in the University of Leeds Driving Simulator [12] where the drivers are intentionally subjected to stressful driving conditions caused by time pressure and surrounding traffic conditions. Their choices are recorded alongside physiological measurements of stress indicators (skin conductance and heart rate variability), self-reported stress levels, attitudes (risk taking propensity) and observed characteristics (age, gender, experience).

The particular focus of this paper is on gap-acceptance decisions in unsignalised junction where the perpendicular stream of traffic has priority. The drivers have to cross this intersection first under normal and later under stressful condition (when he/she is subjected to time pressure). Separate gap acceptance models are developed for the stressed and normal scenarios (differentiated based on physiological measurements). Statistical tests of model parameters reveal significant differences in model parameters in the two scenarios The combined data is then used to develop hybrid choice models of gap-acceptance decisions where the utility of a gap is influenced by the latent state stress level of the driver (indicated by physiological indicators) as well as the attributes of the gap, his/her observed characteristics and latent risk-taking propensity (indicated by the Driving Stress Inventory [13]). The estimation results also confirm the significant effect of stress on gap-acceptance decisions.

The current study is a first step towards modelling the effects of stress on drivers’ behaviour. In addition to better models for evaluation of safety, the model provides a general framework that can be used for integrating the effect of mental states inferred from physiological measurements in choice models in other areas beyond a transport context.

 

References:

  1. Deldin, P. J., & Levin, I. P. (1986). The effect of mood induction in a risky decision-making task. Bulletin of the Psychonomic Society, 24(1), 4-6.
  2. Yuen, K. S., & Lee, T. M. (2003). Could mood state affect risk-taking decisions?. Journal of affective disorders, 75(1), 11-18.
  3. Simon, H. A. (1987). Making management decisions: The role of intuition and emotion. The Academy of Management Executive (1987-1989), 57-64.
  4. Kahneman, D., Krueger, A. B., Schkade, D. A., Schwarz, N., and Stone, A. A. (2004) “A Survey Method for Characterizing Daily Life Experience: The Day Reconstruction Method”, Science (December 3, 2004), pp. 1776-1780.
  5. Hektner, J. M., Schmidt, J. A., and Csikszentmihalyi, M. (2006) Experience Sampling Method: Measuring the Quality of Everyday Life, Sage Publications, Inc.
  6. Anders Karlstroem (2014): A computational neuroeconomic model of hypothetical bias, Paper presented in International workshop on current topics in choice modelling, Choice Modelling Centre, University of Leeds.
  7. Woodford, M. (2014). An Optimizing Neuroeconomic Model of Discrete Choice (No. w19897). National Bureau of Economic Research.
  8. Toledo, T. (2007). Driving behaviour: models and challenges. Transport Reviews27(1), 65-84.
  9. Choudhury, C. F. (2007). Modeling driving decisions with latent plans (Doctoral dissertation, Massachusetts Institute of Technology).

10.  Lancaster, R., & Ward, R. (2002). The contribution of individual factors to driving behaviour: Implications for managing work-related road safety. HM Stationery Office.

11.  Ge, Y., Qu, W., Jiang, C., Du, F., Sun, X., & Zhang, K. (2014). The effect of stress and personality on dangerous driving behavior among Chinese drivers. Accident Analysis & Prevention73, 34-40.

12.  http://www.uolds.leeds.ac.uk/

13.  Gulian, E., Matthews, G., Glendon, A. I., Davies, D. R., & Debney, L. M. (1989). Dimensions of driver stress. Ergonomics32(6), 585-602.


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