Introduction*
World Health Organization (WHO) revealed 48 percent of victims who died were the productive ages (15-44 years). The traffic accident victims with high school education (SLA) occupy the most numbers. Data from the Indonesian Police Traffic Corps shows the percentage of victims with a high school education background reached 57 percent.
Supporting this research, as well as to find out the factors that
most influence the characteristics of drivers at young age or more
precisely in the "Z" generation, an observation survey was conducted
on drivers who fall into that category, namely at the age of 16-21 years.
* Corresponding author.
E-mail address: [email protected]
Article with open access
under license
Method
Analysis Variabel
The study was conducted with a basic hypothesis in which there are some differences in driving characteristics among Z Generation. The characteristics that will be observed as well as being dependent variables include the followings.
1. Reaction Time in seconds (s)
2. Measured as the interval between initial scenarios for the first indication of an acceptable response gap
3. Driving agility
4. Anticipation
5. Take Decisions as Conflict Reactions
6.
Color Blindness
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7. The vision function of the driver in receiving information
8. The sequence of responses when dealing with various conflicts that are simulated
Meanwhile, the independent variables are needed to see the factors that cause the distinguishing characteristics of driving among Z Generation. Some of the suspected factors are:
1. Gender
2. Age
3. The range of time, the respondent can drive
4. Music genre used while driving
5. Hobbies in play games
6. Use of cellular phones when driving
Instrument Collecting Data
The Data Collection Equipment is a driving simulator. The driving simulator consists of a series of driver assessments of various simulated road environment situations and a data acquisition system of driving results. The driver section of the simulator consists of a car seat, steering wheel, dashboard and pedals.
The stimuli are in the form of a device with 3 monitor screens in the form of front view and rear view mirror situations, right side left and side mirrors. The simulator that will be used is the bus driving simulator at the Road Transportation and Road Transportation High School Laboratory, as shown in the following figure
Picture 1.
TeknoSim Bus Driving
Simulator
Analysis Method
1. Cross Tab Analysis Method
* Generation Z Is a generation after Y Generation,
which is define as people born in the years from 1998 to 2010
Cross tab analysis method is carried
out to find out the relationship between the independent variable and the
dependent variable which is suspected to be the dominant cause of accident in
generation Z*.
Observations made on STTD cadets as objects of research were carried out through 2 (two) stages, namely pre-observation in order to provide practical experience in recognizing the basics of the driving process and the second stage was observing by simulating various scenarios contained in the simulator module. The assessment of driving ability using a simulator produces several variables can be seen in the following table.
Table 1.
Research Variables
|
NO |
DEPENDENT VARIABLES (Y) |
INDEPENDET VARIABLES (X) |
|
1 |
Turn without indicators |
Reaction time |
|
2 |
Moving lane is incorrect |
Dexterity |
|
3 |
Violation of signs |
Anticipation |
|
4 |
Too fast |
Decision-making |
|
5 |
Stop in a restricted parking
area |
Color blind |
|
6 |
Driving in the wrong lane |
Vision |
|
7 |
Cross the yellow line |
|
|
8 |
Stop line violations |
|
|
9 |
Violation honking |
|
Sumber: Analysis References 2020
2. Chi Square Analysis Method
The analysis was carried out in identifying the relationship between independent and bound variables using the cross tab analysis method and then testing the statistical data using the chi-square method to determine the ratio of the relationship between these variables. The analysis was performed using statistical data processing software aids with a confidence level of 95% or α = 0.05 whose results will be described as follows. The hypothesis of this research is.
H0 : there is no influence between the independent variables (clinical characteristics of the driver) with the dependent variable (stimuli driving behavior using a simulator)
H1 : there is influence between independent variables (clinical characteristics of the driver) with the dependent variable (stimuli driving behavior using a simulator)
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57
results and discussion
1.
Scenario 1 (Eco* / Smart Drive in Urban
Conditions with Low Traffic)
The first scenario is the observation of
the object of study in driving by using the Eco/Smart Drive function in urban conditions with low
traffic.
Relationship Between Variable Driver
Clinical Characteristics With Traffic Violation
Variables in Scenario 1
Table 2.
Relationship
Between Variable Driver Clinical Characteristics With
Traffic Violation Variables in Scenario 1
|
|
Reaction time (X1) |
Dexterity (X2) |
Anticipation (X3) |
|
Turn without
indicators |
0,043 |
0,075 |
0,084 |
|
Moving lane is
incorrect |
0,023 |
0,015 |
0,035 |
|
Violation of signs |
0,016 |
0,047 |
0,27 |
|
Too fast |
0,045 |
0,039 |
0,027 |
|
Stop in a
restricted parking area |
0,38 |
0,025 |
0,01 |
|
Driving in the
wrong lane |
0,94 |
0,016 |
0,04 |
|
Cross the yellow
line |
0,011 |
0,041 |
0,017 |
|
Stop line
violations |
0,19 |
0,72 |
0,04 |
|
Violation honking |
0,037 |
0,56 |
0,021 |
|
|
Decision-making (X4) |
Color blind (X5) |
Vision (X6) |
|
Turn without
indicators |
0,012 |
0,57 |
0,12 |
|
Moving lane is incorrect |
0,012 |
0,14 |
0,67 |
|
Violation of signs |
0,034 |
0,012 |
0,025 |
|
Too fast |
0,017 |
0,36 |
0,089 |
|
Stop in a
restricted parking area |
0,013 |
0,077 |
0,23 |
|
Driving in the
wrong lane |
0,021 |
0,044 |
0,027 |
|
Cross the yellow
line |
0,37 |
0,036 |
0,012 |
|
Stop line
violations |
0,018 |
0,022 |
0,028 |
|
Violation honking |
0,039 |
0,085 |
0,16 |
Based on the statistical test results using the
Chi-Square test method, the strongest relationship between independent
variables and dependent variables is indicated by the Asymp.Sig
value. (2-sided) ≤ α = 0.05, that is, between the Decision
Making variable (Y) and the Inappropriate Moving Line (X) variable with
the Asymp.Sig value. (2-sided) 0.01.
* Eco
How to drive that aims to optimize fuel consumption efficiently and play a role in reducing the risk of accidents on the highway
Picture 2.
Radar Diagram of Relationship Between Variable
Clinical Characteristics of Drivers with Traffic Violation Variables in
Scenario 1
Based on the statistical test results using the
Chi-Square test method, the strongest relationship between independent
variables and dependent variables is indicated by the Asymp.Sig
value. (2-sided) ≤ α = 0.05, that is, between the Sudden Braking variable (Y)
and the Agility variable (X) with the Asymp.Sig
value. (2-sided) 0.014
Picture 3.
Radar Diagram of Relationship Between Variable
Clinical Characteristics of Drivers with Driving Violation Variables in
Scenario 1
Based on the statistical test results using the
Chi-Square test method, the strongest relationship between independent
variables and dependent variables is indicated by the Asymp.Sig
value. (2-sided) ≤ α = 0.05, namely between the Collision variable with Other
Vehicles (Y) and the Reaction Time variable (X) with the Asymp.Sig
value. (2-sided) 0.012.
Picture 4.
Radar
Diagram of Relationship Between Clinical Characteristics of Drivers with
Defensive Driving Violence Variables in Scenario 1
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2.
Scenario 2 (Eco / Smart Drive in Urban
Conditions with Heavy Traffic)
The second scenario is the
observation of study objects in driving by using the Eco/Smart Drive function in urban conditions
with heavy traffic.
Table 3.
Relationships
Between Variable Clinical Characteristics of Drivers With
Traffic Violation Variables in Scenario 2
|
|
Reaction time (X1) |
Dexterity (X2) |
Anticipation (X3) |
|
Turn without indicators |
0,021 |
0,135 |
0,072 |
|
Moving lane is incorrect |
0,037 |
0,055 |
0,019 |
|
Violation of signs |
0,027 |
0,038 |
0,41 |
|
Too fast |
0,048 |
0,042 |
0,038 |
|
Stop in a restricted parking area |
0,57 |
0,021 |
0,07 |
|
Driving in the wrong lane |
0,85 |
0,013 |
0,052 |
|
Cross the yellow line |
0,006 |
0,028 |
0,009 |
|
Stop line violations |
0,25 |
0,54 |
0,059 |
|
Violation honking |
0,025 |
0,21 |
0,017 |
|
|
Decision-making (X4) |
Color blind (X5) |
Vision (X6) |
|
Turn without indicators |
0,008 |
0,78 |
0,39 |
|
Moving lane is incorrect |
0,035 |
0,09 |
0,49 |
|
Violation of signs |
0,072 |
0,049 |
0,031 |
|
Too fast |
0,028 |
0,73 |
0,26 |
|
Stop in a restricted parking area |
0,045 |
0,038 |
0,4 |
|
Driving in the wrong lane |
0,016 |
0,048 |
0,039 |
|
Cross the yellow line |
0,45 |
0,041 |
0,008 |
|
Stop line violations |
0,027 |
0,047 |
0,037 |
|
Violation honking |
0,014 |
0,059 |
0,09 |
Source:Analysis Result Of 2020
It can be seen in the table above that the
strongest relationship between the independent variable and the dependent
variable is indicated by the Asymp.Sig value.
(2-sided) ≤ α = 0.05, that is, between the Collision variable and Horn
Violation Violation (Y) and the Decision
Making variable (X) with the Asymp.Sig value.
(2-sided) 0.014.
Picture 5.
Radar Diagram of Relationship Between
Variable Clinical Characteristics of Drivers with Traffic Violation Variables
in Scenario 2
It can be seen in the following figure that
the strongest relationship is between the independent variable and the
dependent variable shown by the Asymp.Sig value.
(2-sided) ≤ α = 0.05, that is, between the collision variable with sudden
braking (Y) and the anticipation variable (X) with an Asymp.Sig
value. (2-sided) 0.011.
Picture 6.
Radar
Diagram of Relationship Between Variable Clinical Characteristics of Drivers
with Driving Violation Variables in Scenario 2
Based on the test results statistically
using the Chi-Square test method the strongest relationship between the
independent variable with the dependent variable indicated by the Asymp.Sig value. (2-sided) ≤ α = 0.05, that is, between
Inaccuracy Maintaining Distance (Y) and Agility variable (X) with Asymp.Sig values. (2-sided) 0.017.
Journal of Social Science |
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Picture 7.
Radar
Diagram of Relationship Between Variable Clinical Characteristics of Drivers
with Defensive Driving Violence Variables in Scenario 2
CONCLUSION
The characteristics of the Stimuli of "Z" Generation most dominant causes of accidents descriptively from the simulator test
results are as follows.
1.
In the
condition of Eco/Smart Drive for very quiet or low traffic that the
"Z" Generation driver has the following characteristics:
a. Tend to move lane in conditions that are not right
with the value of Asymp.Sig. (2-sided) 0.01.
b. Tend to brake suddenly with very minimal dexterity
with Asymp.Sig value. (2-sided) 0.014
c. Ponit a and b cause a tendency to collide with other vehicles with a very
short reaction time without agility of 0.012 minutes with an Asymp.Sig value. (2-sided) 0.012.
d. have not been able to improve how to drive and have
not been able to prevent wasteful consumption of fuel even in non-congested
traffic
2.
In the
condition of Eco/Smart Drive for heavy or high traffic that the "Z"
Generation driver has the following characteristics:
a. Tends to honk with Asymp.Sig
value. (2-sided) 0.014
b. Tend to brake suddenly with a very small braking
distance or too close to the vehicle in front of him and very minimal dexterity
with the value of Asymp.Sig. (2-sided) 0.017
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ISSN: 1947-1017. School of Manufacturing Science and Engineering, Sichuan
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