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Predicting the Future Driving Style of Novice Drivers:

The Role of Self-Evaluation and Instructors’ Ratings Following

Driver Training

L. Šeibokaitė, A. Endriulaitienė, R. Markšaitytė,

K. Žardeckaitė-Matulaitienė, A. Pranckevičienė

Vytautas Magnus University, Kaunas, Lithuania

Background

• Novice drivers - the most vulnerable group.

• The first year after licensing - the highest risk of being involved in a vehicle crash.

• The identification of crash-prone drivers prior to licensing remains problematic.

Background (cont.)

• Risk taking behaviour is detectable even before licensing.

– self-assessment at the end of training

– instructors’ or examiners’ ratings

– behaviour in a driving simulator.

• Evidence regarding validity is insufficient.

They make quite accurate judgements of own driving

style and skills

Tend to overestimate their own driving skills

They make accurate judgements of trainees’ driving skills. Their job

involves teaching those skills

The minor value of instructor’ ratings to explain trainees’ driving behaviour.

The focus of the driver education and licensing

system is on vehicle manoeuvring skills

rather efforts to maintain traffic safety

Boccara et al., 2011; Groeger, 2001;

Mynttinen et al., 2009; Victuar et al., 2005;

de Winter, 2013

Question

Whether driving instructors or learner drivers themselves are able to predict future risks associated with driving?

Previous research

Focused mainly on evaluation of

driving skills. Driving style was

ignored.

Proposed only cross-sectional

data, which failed to answer if an

effect remains in long run

Aims of the study

(a) to examine congruence of the evaluations of driving skills between instructors and learner drivers themselves immediately after completing the training;

(b) to compare the predictive value of self-assessed and instructor‘s assessed driving skills for the prediction of future self-reported risky driving behaviour, drivers’ self-efficacy and the outcomes of risky driving.

Driver education system in Lithuania

• Legal licensing age – 18 years.

• Duration – 1-3 months

• Content:– Knowledge (no less 40 h.):

• Traffic rules• First medical aid

– Driving skills in real road with instructor (no less 20 h.)

Participants Licensed novice drivers – 78.

46 % 54 %

Age 17 – 29 years. Mean age – 19 52 % - 18-19 years at the end of trainning.

Several driving schools across Lithuania, but were mainly recruited from the large cities.

Procedure

Supposed to have been driving for 6 months. 75 percent reported driving 1-3 times per

week or less frequently

Measures• Self-reports:

– Time 2:• the Adelaide Driving Self-efficacy scale –

ADSES (George, Clark, & Crotty, 2005), which measure the confidence of the driver in being able driving well in various situations (Cronbach alpha = .93).

– Time 3:• self-reported risky driving behaviour using the

Driver Behaviour Questionnaire (DBQ; Parker et al., 1995): errors (Cronbach’s alpha = .71) and violations (Cronbach’s alpha = .77).

• involvement in an at fault crash, involvement in a crash where people were injured, stopped by the police due to traffic rule violations.

Measures (cont.)• Instructors’ ratings:

– Time 2:• Evaluation of the current driving skills of

their trainees (1 = Very weak to 5 = Very proficient).

• Prediction of future behaviour on the road. Probability (from low to high) of the trainee:

– driving in a risky manner, – making errors, – violating traffic rules, – being involved in a motor vehicle crash, – and being fined for rule violations.

Results: Correlation between driving self-efficacy and instructors’ ratings

Instructors’ ratings

Self-efficacy

Males Females

Driving skills -0.028 -0.128

Prediction: take risk 0.361 -0.007

Prediction: violate rules 0.273 -0.056

Prediction: make errors -0.174 -0.028

Prediction: involved into accident 0.086 0.024

Prediction: fined by police 0.115 -0.089

Results: Prediction of male traffic rule violations

Predictor variable B Beta t Sign.Self-efficacy .010 .047 .244 .810Driving skills -.863 -.172 -.720 .481Prediction: make errors -.939 -.271 -1.040 .312

Prediction: involved into accident 3.057 .658 2.946 .009

Results: Prediction of female traffic rule violations

Predictor variable B Beta t Sign.Self-efficacy .014 .067 .416 .688Driving skills 10.820 1.558 5.085 .001Prediction: take risk 4.645 1.348 4.336 .002Prediction: violate rules 1.923 .483 2.323 .049Prediction: make errors -.053 -.009 -.055 .957Prediction: involved into accident 1.616 .262 1.421 .193

Prediction: fined by police .143 .027 .136 .895

Results: Prediction of male driving errors

Predictor variable B Beta t Sign.Self-efficacy -.123 -.464 -2.230 .039Prediction: take risk 1.866 .483 2.069 .053Prediction: make errors -3.340 -.776 -2.863 .010

Prediction: involved into accident 3.106 .538 2.437 .025

Results: Prediction of female driving errors

None of predictor factor could explain driving errors.

Results: involvement into negative driving outcomes

• Only 6 out of 78 drivers reported one of several negative driving outcomes:– 2 were at fault of vehicle accidents

(males, 18-19 years)– 5 were stopped at least once by police

due to traffic rule violations (4 males, 1 female; 18-23 years)

Results: description of drivers at fault for accident

Driving errors

15 17.3 20 25 34

1 2

8 8 10 12.8 22

1 2Rule violations

48 77.5 91.5 105.5 116

1 2Driving self-efficacy

Results: description of drivers at fault for accident (cont.)

1 Percentile 2 Percentile

Will pass exam in first attempt yes - no -

Driving skills 3 25 3 25

Prediction: take risk 1 25 2 50

Prediction: violate rules 1 50 2 75

Prediction: make errors 3 75 3 75

Prediction: involved into accident 2 75 4 above 75

Prediction: fined by police 2 50 3 75

Results: description of drivers stopped by police

Driving errors

15 17.3 20 25 34

3 6

8 8 10 12.8 22

56Rule violations

48 77.5 91.5 105.5 116

3 4Driving self-efficacy

4 5

34

56

Results: description of drivers stopped by police (cont.)

3 Percentile

4 Percentile

5 Percentile

6 Percentile

Will pass exam in first attempt

no - no - yes - yes -

Driving skills 3 25 3 25 4 50-75 4 50-75

Prediction: take risk 1 25 1 25 2 50 2 50

Prediction: violate rules 1 50 1 50 2 75 1 50

Prediction: make errors 3 50-75 4 above 75

3 50-75 2 25-50

Prediction: involved into accident

3 above 75

2 75 2 75 2 75

Prediction: fined by police

3 75 2 50 2 50 1 bellow 25

Conclusions, discussion

• There was only a minor degree of congruence in the evaluation of driving skills between driving candidates and their instructors. – Men with higher driving self-efficacy were

perceived by instructors to be at higher risk of driving in an inappropriate manner.

Self-confidence in driving just after compulsory practise sessions might signal instructor that trainee will risk more as he/she believes to handle situation as beeing good driver.

Supported

by other

studies

It is difficult to state whether candidates fail to objectively evaluate their own ability to drive or whether instructors are not able to recognise

differences in driving skills

Different measurements were used for trainees and instructors.

Conclusions, discussion (cont.)

• Self-reported driving violations during the first six months of independent driving were predicted by several ratings made by the instructors: expectation of trainee to be involved in a motor vehicle crash, violating rules and driving in a risky manner.

The evaluations of the instructors seemed to have good validity. Instructors are able to recognise at risk drivers at the very beginning of driving.

Supported

by other

studies

Instructors are able to predict certain type of driving behaviour (if

they predict violations, trainees tend to violate

rules later)


• Instructors’ prediction of future driving errors was supported by data only for males. – Those drivers, who made more errors

while driving were evaluated by instructors, before took their license, as being more at risk of: being involved in a motor vehicle crash, making errors, and driving in a risky manner.

– None of the measured predictors were able to predict driving errors among females.

Again, instructors’ predictions were behaviour

specific. They expected trainees making driving

errors, and trainees reported more frequent

errors while driving later.

Gender stereotypes?


• Lower driving self-efficacy after driving trainning served as a good predictor of driving errors in the beginning of independent driving among males, but it was not a good predictor of intentional driving violations.

Self-report of driving candidates could be used in identification of lower driving skills

It seems common sense that driving self-

efficacy and self-reported future errors were related, as they

are conceptually similar

For prediction of traffic rule violations other self-reported

measures should be used.

What to do with the results?

• Drivers at risk could be recognised or predicted just before they receive driving license.– License candidates are able to report

their own weaknesses in driving activity.

– Ratings and predictions of their instructors are even at greater value.

What to do after, when future drivers at risk have been identified?

Additional trainning might rise self-confidence in

driving skills of the trainee, therefore, increase the

possibility of intentional risk on the road.

Termination of licensing until they

mature out their unnecessary risk

taking?

Some other ideas?

Thank you for your attention

Special thanks to

J. Marcinkevičienė, R. Arlauskienė and A. Stelmokienė for helping with data collection.

For communication: [email protected]

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