چكيده لاتين :
Background and aims: Worldwide statistics declare that 1.2 million people are killed as a
result of a road traffic accident (RTA). Besides, 20-50 million are injured every year. Traffic
accidents account for leading causes 1.2% of deaths and 23% of injuries. In Iran, 16,000
people are annually cut short in traffic accidents. A great number of people suffer from nonfatal
injuries/disabilities, which is estimated not to be less than 335,000. Statistics show that
road traffic mortality in Iran has been higher than the global average, which can dramatically
increase direct (medical costs, caring disabled people, broken cars, and etc.) and indirect
(PTSD, traffic jam, depression in families, losing powers (permanently or temporarily) and
etc.) costs. The magnitude of RATs in Iran can be better understood if it is mentioned that
accidents are in the position of first place of the ranking of lost years of life. This caveat calls
for a focus for urgency in RATs control strategies as a main focal point to improve public
health in Iran.
Nowadays, a triple-causal approach (vehicle, environment, and human) was accepted in
traffic incidents. During recent years, vehicle and environment (as causal factors of traffic
incidents) have been greatly improved. In contrast, human behavior has still remained as the
most frequent contributing factor (up to 94%). This factor is being the most important and
critical factor within any system, including traffic system.
In a dangerous traffic situation, which is mostly no room to commit any error, accident
prevention remains on drivers' abilities and skills. It seems human factors can play a special
role to control human behavior. Therefore, it worth to provide a human factor view on
human behavior while driving. During driving, the driver has to collect a large amount of
information at any given time and put them in the process of continuous decision making. If
any functions of the driver's decision-making process cannot be at the optimum level, human
error may lead to catastrophic consequences. Driver errors are usually outcomes of mismatch
between driving demands and driver abilities, especially psychologic ones. It is better to say
human error is mostly the result of defects or improper function of mental information
processing, which is familiar to people as forgetfulness, inattention, carelessness, negligence,
recklessness, and etc.
A group of methodologies which has been introduced in human factors are able to predict
cognitive drivers’ errors. Despite the importance of role of human error in traffic accidents,
Human error identification methods (HEIs) have been mostly used in high risk environments
such as airline industry. Yet, there is few published studies on identification of driver’s errors
and transportation industry. The purpose of this study is to predict driver errors using Human
Error Template (HET) method meanwhile a real driving task.
Methods: This case study was carried out to identify and predict the drivers' error in a
specific real driving scenario. At first, the scenario was designed based on agreement of
research team members. To fulfill the scenario, an Iranian car (PARS Peugeot) was used
which had successfully passed the technical test (with regard to lights, mirrors, glasses,
safety belts, horn, oil, outlet emissions, side slides, shock absorbers, brakes, steering,
suspension system lever). The driver was a healthy 42 years old man with 20 years driving
experience as his career. A direct route with proper traffic signs was considered on a twoway
street with mild traffic flow on a sunny day. The scenario was as: “the driver departs
from the park and moves in a pre-determined direction. A few moments later, he speeds up
to overtakes the front car. Then, he simply continues driving. By getting the destination, he exits the path and park the car”.
Since driving consists of several sub-tasks which should be performed simultaneously, a list
of sub-tasks which are required to complete the scenario was provided by interviews, as well
as, direct observation. The tasks were analyzed based on Hierarchical Task Analysis (HTA).
Then, the tasks list was used as the input for the HET technique. This technique identifies
and classifies external errors (EEMs) in the form of human error detection methods (HEIs).
The technique considers EEMs in 12 patterns that are described as follows: 1. Fail to
execute, e.g. the driver cannot correctly get the clutch 2. Incomplete task execution, e.g.
leaving handbrake in middle position 3. Task execution in a wrong direction, e.g. pressing
the gear lever in a wrong direction 4. Wrong task execution, e.g. suddenly change direction
5. Task repeated, e.g. placing the lever twice 6. Task execution on the wrong interface
element, e.g. pressing a pedal instead of the others. 7. Task execution too early, e.g. turn on
the guide much earlier than the redirection. 8. Task execution too late, e.g. delay in coming
back to appropriate line and staying in the overtaking line. 9. Too much task execution, e.g.
continuous gear changing 10. Too little task execution, e.g. few frequencies of using brakes
11. Misread information, e.g. misreading information on speed gauge 12. Other. Next, errors
types and potential consequences were described.
Results: Hierarchical task analysis (HTA) showed that for implementation of the scenario,
three main tasks and nine sub-tasks were needed. Among 101 errors detected by HET, 16%
were considered as unacceptable errors. Also, the most common types of errors included:
“Task execution incomplete” (25.74%), “Task executed too late” (21.78%) and “Fail to
execute” (13.86%), respectively. The results from the distribution of human errors in the
main tasks and sub-tasks indicate that the main task “scrolling the route” accounts for 48%
of all errors. Distribution of this ratio for “acceleration changing”, “line changing” and
“distance adjusting” was 49%, 33% and 18%, respectively. Rate of errors were 33% in task
of “moving” of which 79% were related to the “starting point of the movement” sub-task,
18% to the “alarms control” sub-task, and 3% to the “fasten seat belt” sub-task. Also, 19% of
errors were found in the “parking” task, which 79% were related to “stopping” sub-task, 16%
related to “shut downing”, and 5% related to “unlocking seat belt”. The distribution of
catastrophic errors for the “scrolling the route”, “moving” and “parking” were found 4, 11,
and 1 error, respectively. These were considered as "unacceptable" errors due to the high rate
of incident occurrence (for example, suddenly line changing, lack of control of one of the
front or rear line, etc.).
Conclusion: The majority of identified type of errors in this study were “Fail to execute”.
Therefore, to reduce this type of error, it is necessary to use corrective actions, such as
periodic retraining drivers, equipping vehicles with visual and audible markers and alarms in
relation to incomplete tasks. These actions can have a beneficial effect on reducing the
severity and chances of occurrence of errors.
Human Error Template (HET) is a comprehensive method that has been used widely in the
aviation industry. As previous studies, the results of this study showed HET is capable to
identify and classify driving errors. Using the technique in traffic domain can provide a great
opportunity to predict drivers’ error. Consequently, there will be a great hope to control them
before-the-fact. In spite of the importance of identifying drivers’ error, a few studies have
been published. It seems the multi-sub-tasks nature of driving has caused the researchers to
avoid involving in HTA for driving. To have a better understand of control strategy in traffic
domain, however, it is strongly recommended to apply human error techniques for driving
situation. Human factors
