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Can’t You Drive!?!: A Better Understanding of the sources of Driver Frustration
Matthew Beck, Stephen Greaves, Dean Rance

Last modified: 13-07-2015

Abstract


Dean Rance, Matthew Beck, Stephen P. Greaves, Institute of Transport & Logistics Studies, University of Sydney, 61-2-91141835(T); 61-2-91141722 (F), matthew.beck@sydney.edu.au stephen.greaves@sydney.edu.au

 

Attributed to increasing congestion, driver personality and the actions of other road users, reported incidents of verbal, physical and other forms of ‘road rage’ are increasing world-wide (Roberts and Indermaur, 2005). Intrinsic to addressing this rising problem is deeper understanding of not just what frustrates motorists, but what they find relatively most frustrating and how this response varies across the population as a prelude to developing interventions and campaigns designed to mitigate the chances of the most serious outcomes from occurring. Traditionally, efforts to understand driver frustration have focused on a likert-scale methodology asking motorists to indicate how frustrating they find particular behaviours. However, these methods invariably result in motorists over-stating their frustration with everything, leading to potentially misleading conclusions about what are the real triggers.

 

Best-worst experiments were developed in the in the late 1990s and have been traditionally applied in consumer choice and marketing experiments (e.g., Flynn et al., 2007). More recently, they have found favour in the transportation field with a particular focus on attitudes towards various issues including public transport, vehicle purchasing and environmental impacts (Beck & Rose, 2014; Beck, Rose & Greaves, 2014). While best-worst methods involve various levels of intricacy, the basic premise is to establish the relative importance of an attribute through a process of forced trade-offs rather than relying on typical scale based responses which are used to infer this. The best-worst methodology is particularly pertinent for the current application, where it might reasonably be expected that respondents state that all behaviours are frustrating (that is to say that respondents may agree with statements), rather than satisfy the objectives of the study which was to identify the most frustrating behaviours.

 

Twenty-six potentially frustrating behaviours were identified by the researchers for the survey based on a review of the literature as well as via a series of in-depth interviews on what motorists found frustrating on the road. The 26 behaviours were organised into three ‘blocks’ of four questions such that the optimal coverage of responses was achieved across the sample population, whilst minimising the response burden for participants. In addition, given the researchers interest in cycling, a fifth question was developed which was common to all three blocks which had four cycling specific behaviours. The survey also included questions around general driving behaviour, demographics and a psychological survey focused around the constructs of ‘excitement’, ‘aggression’ and ‘altruism’, which have been shown to be significant predictors of speeding and aggressive behaviour previously (Greaves and Ellison, 2011).

 

The survey was conducted using an online consumer panel across Australia with 384 valid responses received and included in the analysis. Self-rated driving ability (7.55/10) was significantly higher than the rating of other drivers’ ability (5.54/10), broadly in line with past evidence (McCormick, Walkey and Green, 1986). In terms of the psychological measures, Cronbach alpha scores for the three constructs were all over 0.8 verifying the internal consistency of the underlying questions, with average aggression (54.23/100), excitement (36.83/100) and altruism (74.55/100), largely consistent with those reported in Greaves and Ellison (2011).

 

Initial modelling of the best-worst results showed the most frustrating behaviours were aligned to those actions which put drivers at a relative crash risk (tailgating, being cut off etc.). In addition, careless or perceived discourteous behaviours which delay drivers were also strongly represented (cyclists riding two abreast, vehicles blocking the intersection). Behaviours which are illegal, but may have a social tolerance or acceptability about them (double parking, mobile phone use) were mid-placed in the list of frustrations. In contrast to previous research conducted by GIO (2013), lack of courteous actions such as a driver not giving a thank you wave was not identified as a major or prevalent source of frustration. Perhaps surprisingly given a relatively negative public and media perception attached to cycling in many jurisdictions in Australia, behaviours such as riding through red lights and filtering were not strongly represented in the findings.

 

Latent Class Modelling (LCM) was used to uncover whether attitudes differed across the sample and (if so) what the underlying factors might be. Interestingly, hard factors, such as age and gender emerged as non-significant. However, the psychological profiling provided more insight with three broad categories of driver identified. First, was the ‘calm’ driver significantly more frustrated by behaviours which put them at an elevated crash risk (tailgating, cut off) or carelessness (vehicle blocking intersection). Second, was the ‘aggressive/risk taker’ driver who appears to have specific triggers for frustration in the form of other drivers’ neglect and discourteousness (e.g., not letting me merge, not using indicators, taxi stopping suddenly). Third was the ‘average’ driver who is especially tolerable of minority road user groups such as pedestrians and provisional drivers on freeways. This deeper understanding of who gets frustrated by what, has important ramifications for road safety authorities and campaigns focused around mitigating the underlying causes of frustration and road rage.

 

References

 

Beck M and Rose J (2014) The Best of Times and the Worst of Times: A New Measure of Attitudes Toward Public Transport Experiences, 10th International Conference on Transport Survey Methods.

 

Beck, M. J., Rose, J. M., & Greaves, S. P. (2014). I Can’t Believe Your Attitude: Eliciting Attitudes and Beliefs Through Best-Worst Scaling and Jointly Estimating Their Impact on Electric Vehicle Choice. In Transportation Research Board 93rd Annual Meeting (No. 14-5680).

 

Flynn, T. N., Louviere, J. J., Peters, T. J., & Coast, J. (2007). Best–worst scaling: what it can do for health care research and how to do it. Journal of health economics26(1), 171-189.

 

GIO (2013) Research Finds Driver Discourtesy Rife in NSW, GIO, published 15 July 2013, viewed 28 October 2014, available online at <http://www.gio.com.au/news/gio-research-finds-driver-discourtesy-rife-nsw>

 

Greaves, S. P., & Ellison, A. B. (2011). Personality, risk aversion and speeding: An empirical investigation. Accident Analysis & Prevention, 43(5), 1828-1836.

 

McCormick, I. A., Walkey, F. H., & Green, D. E. (1986). Comparative perceptions of driver ability—a confirmation and expansion. Accident Analysis & Prevention18(3), 205-208.

 

Roberts, L. D., & Indermaur, D. (2005). Social issues as media constructions: The case of ‘road rage’. Crime, Media, Culture1(3), 301-321.

 

Shinar, D. (1998). Aggressive driving: the contribution of the drivers and the situation. Transportation Research Part F: Traffic Psychology and Behaviour,1(2), 137-160.


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