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The impact of the age of the driver of freight transport on the 

probability of being involved in a traffic accident (case study on 

the Krian – Taman Sidoarjo Road) 

Dwi Risdianto1*, Hera Widyastuti1   

1Department of Civil Engineering, Faculty of Civil, Planning, and Geo Engineering, 

Institut Teknologi Sepuluh Nopember, Surabaya, East Java, Indonesia 

dwirisdianto06@gmail.com 

Received 08-07-2022; accepted 27-09-2022 

Abstract. A traffic accident is one of the traffic problems that cause deaths, injuries, and material 

loss. The high number of traffic accidents indicates the low level of road traffic safety. Krian – 

Taman Sidoarjo road has become one of the black site areas. According to data from the Sidoarjo 

Resort Police, there have been 66 traffic accidents from 2015 to 2020. The majority of accidents 

involve freight transport compared to passenger transport. Therefore, it is necessary to research 

the factors of freight transport drivers that affect the possibility of traffic accidents on the Krian 

– Taman Sidoarjo road. Methods of data collection using questionnaires to drivers of freight 

transportation. While the analytical method used is descriptive analysis and logistic regression 

using data from questionnaires. The research shows that the older the driver, the higher the 

probability of being involved in a traffic accident. 

Keywords: freight transport, age, probability of traffic accident 

1.   Introduction 
Traffic accidents have become a global issue, where almost 1,35 million people die yearly, and 50 

million people are seriously injured [1]. Most victims who died from traffic accidents were in their teens 

and productive ages, that is, the age of 5 – 29 years [1]. Of the number of victims of traffic accidents, 

90% occur in developing countries, including Indonesia. Traffic accidents are the third biggest killer in 

Indonesia after coronary heart disease and tuberculosis (TBC) [2]. According to data from the 

Indonesian National Police in 2017, an average of 3 people die every hour due to traffic accidents in 

Indonesia. The data also states that a large number of accidents is caused by several factors, including 

61% by the human factor (related to the ability and character of the driver), 9% by vehicle factors (related 

to meeting technical requirements and roadworthiness) and 30% by infrastructure factors and 

environment [3]. The human factor is the most dominant cause of traffic accidents. One of the causes 

that often occur is traffic violations, such as: violating road signs and markings, speeding, overloading 

and over-dimension, and so on. 

Traffic accidents are events that are difficult to predict when and where they will occur. Accidents 

do not only result in trauma, injury, or disability but also death [4]. The impacts caused by traffic 

accidents include deaths and injuries, material losses [5], and traffic jams. In addition, traffic jams can 

cause a loss of time value and waste fuel and the environment on the road [6]. 



 

 

 

 
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Efforts made by the Government to reduce the number of traffic accidents in Indonesia by launching 

the Decade of Action for Road Safety 2011 – 2020 and the National General Plan for Road Safety 2011 

– 2035. This program is the result of joint work between relevant agencies (Ministry of Transportation, 

Ministry of Public Works, Indonesian National Police, Ministry of Health, Ministry of National 

Education, Ministry of Home Affairs, Ministry of Industry, Ministry of Finance, and National Planning 

and Development Agency). The National General Plan for Road Safety is structured as government 

responsibility to ensure road traffic safety [7-8]. 

Sidoarjo regency is one of the regencies located in East Java Province, which is directly connected 

to Surabaya city, Gresik regency, Pasuruan regency, and Mojokerto regency. Sidoarjo is one of the main 

supports for Surabaya city and is included in the Gerbangkertosusila area. Based on the Sidoarjo Resort 

Police, in 2019, the fatality of the victim dying was 219 people [9]. One of the recent traffic accidents 

is on the Krian – Taman Sidoarjo road, a national road connecting Mojokerto regency and Surabaya 

City, East Java. Traffic conditions on this road are quite congested, dominated by motorbikes and freight 

transport. This road is a route for trucks to transport goods from Krian to Surabaya and its surroundings 

because the land use around Krian and Trosobo is an industrial and warehousing area. 

Based on data from the Sidoarjo Resort Police, from 2015 to 2020, there were 66 traffic accidents on 

the Krian – Taman road, which resulted in 33 deaths, 19 serious injuries, and 30 minor injuries. Of the 

66 accidents involving freight transport, about 68 vehicles and passenger transport, 28 vehicles [10]. 

The high number of traffic accidents on the Krian – Taman road has resulted in this road being known 

as “The Black Site Area”. 

The main problem in this study is that freight transport contributes more to traffic accidents than 

passenger transport. So it is necessary to improve the factors that cause it [11]. The human factor plays 

an important role in reducing the number of traffic accidents on the road. Therefore, this research’s 

object is the driver of freight transport. The study aims to identify the causative factors and to know the 

model and the probability of traffic accidents on the Krian – Taman Sidoarjo road. 

2.   Material and Methods 
This section includes procedures, procedures, or work stages used to obtain research purposes. This 

section aims to make research run more smoothly, systematically, and credibly. 

2.1.  Time and Location 

This research is conducted in the Service Unit of Motor Vehicle Weighing Trosobo Sidoarjo. The 

basis of consideration is that every freight transport crossing Krian – Taman Sidoarjo road must carry 

out inspections and weigh vehicles according to applicable regulations. Research implementation time 

for several days (weekdays) during operating hours.  

2.2.  Data Collection Stage 

This stage aims to obtain the information needed to achieve the research purposes. Techniques used 

to collect data in this study include interviews, questionnaires, and documentation.   

2.2.1.  Primary Data. This data is obtained from interviews or direct questions and answers to 

respondents and filling out questionnaires through Google forms. Respondents are randomly taken to 

freight transport drivers passing through Krian – Taman Sidoarjo road. The technique used is to reveal 

a preference. 

• Number of Samples 
The population used in the study were all freight drivers who passed on the Krian – Taman Sidoarjo 

road. While taking the number of samples refers to the average daily traffic of freight transport at the 

Service Unit of Motor Vehicle Weighing Trosobo Sidoarjo. In calculating the number of samples using 

the Slovin formula. The average daily traffic of freight transport in 2020 at the Service Unit of Motor 

Vehicle Weighing Trosobo Sidoarjo (N) is 3.558 [12]. Error tolerance limit or margin of error (e) is 

10%. So that the number of samples (n) is obtained: 



 

 

 

 
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𝑛 =
𝑁

1 + 𝑁𝑒2
=

3.558

1 + 3.558 (0,1)2
= 100 𝑠𝑎𝑚𝑝𝑙𝑒𝑠 

 

The minimum number of samples required in this study is 100 samples. However, to anticipate 

incomplete/ invalid respondents’ answers, 150 samples are used. 

2.2.2.  Secondary Data. This data is used to support research. These data include: 

1. Data on traffic accidents from 2015 to 2020 on the Krian – Taman Sidoarjo road (Police Resort 
Sidoarjo). 

2. Daily traffic freight transport data (Service Unit of Motor Vehicle Weighing Trosobo Sidoarjo). 
3. Data of violations freight transport (Service Unit of Motor Vehicle Weighing Trosobo Sidoarjo). 
4. Data of Krian – Taman Sidoarjo road. 

2.3.  Data Analysis Stage 

Primary and secondary data from the field survey are then processed for further analysis. Secondary 

data is used to describe the characteristics of traffic accidents on the Krian – Taman Sidoarjo and the 

causal factors. At the same time, the primary data from the questionnaire results have then analyzed the 

characteristic of the respondents using descriptive statistics in the form of pie charts. Finally, we are 

using logistic regression analysis to determine the model and probability of traffic accidents on the Krian 

– Taman Sidoarjo road. 

2.3.1.  Traffic Accidents Data Analysis. This analysis will describe the causes of traffic accidents and 

the characteristics of traffic accidents on the Krian – Taman Sidoarjo road. 

1. Analysis of Traffic Accidents Causes 
The method used in the fishbone diagram. In this study, the causes of traffic accidents are divided into 

4 (four), namely: human, vehicle, road, and the environment [13], as shown below: 

 

 

Figure 1. Fishbone Diagram of Traffic Accidents on 

the Krian – Taman Sidoarjo road. 

 

 

2. Analysis of Traffic Accidents Characteristics 
The method used is the “5W + 1H” approach issued by the Department of Settlements and Regional 

Infrastructure [14], namely: Why (factor of accident), What (type of vehicle), Where (location of 

accident), Who (involvement of road users), When (time of occurrence) and How (type of vehicle 

movement). 



 

 

 

 
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2.3.2.  Analysis of Respondents’ Characteristics. This analysis will describe the characteristic of drivers 

of freight transport passing through the Krian – Taman Sidoarjo road using descriptive statistical 

methods in the form of pie charts. 

2.3.3.  Analysis of Traffic Accident Modeling and Probability. This analysis used logistic regression with 

the help of SPSS Software. The first step that must be taken is determining the independent and 

dependent variable. 

 

Table 1. Independent and Dependent Variable in Research. 

Independent Variable 

X1 = Age 

Dependent Variable 

Y = Involvement in Traffic Accident on the Krian – Taman Sidoarjo road 

In determining the model of logistic regression equation using the following formula: 

 

 
π (𝑥) =

𝑒𝛽0 + 𝛽1 𝑥1+ …...+ 𝛽𝑝 𝑥𝑝  

1 +  𝑒𝛽0 + 𝛽1 𝑥1+ …...+ 𝛽𝑝 𝑥𝑝  
 (1) 

 

With : 

p = number of predictor/ independent variable  

x1, x2, ..., xp = independent variable 

β = independent variable coefficient 

 

Furthermore, to determine the estimated probability of traffic accidents using the following formula: 

 
𝑔(𝑥) = 𝑙𝑛 (

𝜋 (𝑥)

1 −  𝜋 (𝑥)
) =  𝛽0 +  𝛽1 𝑥1 + . . . . . +  𝛽𝑝 𝑥𝑝 (2) 

 
𝑙𝑜𝑔𝑖𝑡(π(𝑥)) =

𝜋 (𝑥)

1 −  𝜋 (𝑥)
=  𝛽0 +  𝛽1 𝑥1 + . . . . . +  𝛽𝑝 𝑥𝑝 (3) 

 

After the above calculation, it will produce an exponential value that will be used to calculate the 

estimated probability value with the following formula: 

 
𝜋0 (𝑥) =

1

1 + exp 𝑔(𝑥)
 (4) 

 
𝜋1 (𝑥) =

exp 𝑔(𝑥)

1 + exp 𝑔(𝑥)
 (5) 

Furthermore, the feasibility test of the logistic regression model was carried out using the Hosmer and 

Lemeshow test (goodness of fit). 

3.   Result and Discussion 

3.1.  Traffic Accident Data Analysis 

3.1.1.  Analysis of Traffic Accident Causes. Based on the analysis of traffic accident causes using a 

fishbone diagram, the following figure is obtained: 

 



 

 

 

 
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Figure 2. Fishbone Diagram Analysis of Traffic Accidents Causes on the Krian – Taman Sidoarjo 

Road. 

3.1.2.  Analysis of Traffic Accident Characteristics. Based on the analysis of the traffic accidents 

characteristics on the Krian – Taman Sidoarjo road from 2015 to 2020 with pie charts, the following 

figure is obtained: 

 

   

Figure 3. Percentage chart of 

traffic accidents causes (Why) 

Figure 4. Percentage chart of 

vehicle type involved in traffic 

accidents (What) 

Figure 5. Percentage chart of 

traffic accidents location 

(Where) 

 

   

Figure 6. Percentage chart of 

road users’ gender in traffic 

accidents (Who). 

Figure 7. Percentage chart of 

road users’ age in traffic 

accidents (Who). 

Figure 8. Percentage chart of 

road users’ jobs in traffic 

accidents (Who). 

 



 

 

 

 
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Figure 9. Percentage chart of 

traffic accidents day (When). 

Figure 10. Percentage chart of 

traffic accidents hour (When). 

Figure 11. Percentage chart of 

vehicle movement types during 

traffic accidents (How). 

The majority of traffic accidents caused on the Krian – Taman Sidoarjo road are human factors, with 

44 accidents (66%). The main causes of human factors including sleepy, speeding, lack of vigilance, 

loss of control, and traffic violations [15]. Most vehicle types involved in traffic accidents on the Krian 

– Taman Sidoarjo road are freight transport, with as many as 68 vehicles (47%); this is because the 

Krian – Taman road is an industrial area that is the route for freight transport to take and receive goods 

from the factory to their destination [16]. Most traffic accidents location occurred on Trosobo road 

(direction Mojokerto – Surabaya) as many as 20 incidents (30%); this is because the geometric condition 

in Trosobo road flyover often causes loaded trucks and buses to experience brake failure [16]. The 

majority of road users in traffic accidents in Krian – Taman Sidoarjo are male, as many as 143 people 

(89%); this is because the average person driving a freight transport vehicle is male [17]. The majority 

of road users aged in traffic accidents in Krian – Taman Sidoarjo are in the age range of 26 to 45 years 

(adults), as many as 90 people (56%); this is because this age is a productive age for people to work and 

carry out daily travel activities [18]. The majority of road users job in traffic accidents on the Krian – 

Taman Sidoarjo are drivers, as many as 71 people (45%); this is because the driver is a job mostly on 

the road, so the risk level of having a traffic accidents is higher than in other jobs [19]. Most of traffic 

accidents day in Krian – Taman Sidoarjo are on Wednesdays and Thursdays, with 13 incidents (20%); 

this is because the two days are working days where the traffic flow through the Krian – Taman Sidoarjo 

road is high quite [15]. Most of traffic accidents hour in Krian – Taman Sidoarjo are in the morning 

(05.00 – 10.00) as many as 22 accidents (33%); this is because these hours are the time for people to 

carry out activities in the morning (school, work, etc.) [18]. The majority of vehicle movement types 

during traffic accidents in Krian – Taman Sidoarjo are front-rear collisions with as many as 20 vehicles 

(30%); this is because the road conditions are quite wide and divided by the median and the low side 

barriers make road users accelerate at high speed so that the front of the vehicle has a high potential for 

accidents [20]. 

3.2.  Analysis of Respondents’ Characteristics 

Based on the results of the questionnaire to 150 respondents obtained the following characteristics: 

 

   

Figure 12. Distribution chart of 

respondents’ age. 

 

Figure 13. Distribution chart of 

respondents’ gender. 

Figure 14. Distribution chart of 

respondents’ education. 

 



 

 

 

 
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189 

 

  

 

Figure 15. Distribution chart of 

respondents’ job. 

Figure 16. Distribution chart of 

the average respondents’ 

income per month. 

Figure 17. Distribution chart of 

respondents’ freight transport 

vehicle types. 

 

 

  

Figure 18. Distribution chart of 

respondents’ vehicle load. 

Figure 19. Distribution chart of 

respondents’ vehicle 

ownership. 

Figure 20. Distribution chart of 

respondents’ reasons for using 

freight transport. 

 

The characteristics of the respondents of freight transport drivers on the Krian – Taman Sidoarjo road 

are dominated by the age of 26 to 45 years about 85 people (56,7%), with the male respondents about 

149 people (99,3%). The last education of respondents is Senior High School/ equivalent about 96 

people (64%), and 98 people (65,3%) were jobs as drivers. The respondents’ average income is Rp. 

2.500.000,- up to Rp. 5.000.000,- per month is about 109 people (72,7%). The most type of freight 

transport vehicle is medium trucks with open/ closed/ tank (conf. axis 1.2 and Permitted Vehicle Weight: 

5.500 to 12.000 kg) about 49 people (32,7%), with vehicle load including general goods (general cargo, 

metal, wood, palletized/ packaged cargo, vehicles with side curtain covers and flat glass) about 75 people 

(50%). The vehicle owned by the Company was about 75 people (50%), and the reason for using freight 

transport is that it was more profitable about 76 people (50,7%). 

3.3.  Analysis of Traffic Accidents Modelling and Probability 

3.3.1.  Testing the Independent Variables on the Dependent Variable of the occurrence of Traffic 

Accidents. Based on the test results of the independent variable to dependent variable in SPSS software 

obtained: 

 

Table 2. Variable Test Results. 

Independent Variable p-value/ Sig. Explanation 

Age (X1) .004 Significant 

 

Table 2 above shows that the age variable has a significant effect on the occurrence of traffic 

accidents because it has Sig. < α that is 0,004 < 0,05.  

3.3.2.  Model and Probability of each Independent Variable on the occurrence of Traffic Accidents. The 

age variable that has a significant effect is then used to determine the model and probability of the results 

of the logistic regression test. 

 



 

 

 

 
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• Variable of Age (X1) 
 

Table 3. The Results of the logistic regression test for the variable of age (X1). 

Variables in the Equation 

 B S.E. Wald df Sig. Exp (B) 

95.0% C.I. for EXP 

(B) 

Lower Upper 

Step 1a X1 .062 .021 8.498 1 .004 1.063 1.020 1.108 

 Constant -3.127 .941 11.031 1 .001 .044   

a. Variable (s) entered on step 1: X1. 
 

 
𝑙𝑜𝑔𝑖𝑡 (𝑝) =  −3,127 + 0,062 Age 
 

For respondents aged 20 years, the probability obtained is: 
 

𝑝 =
𝑒−1,887  

1 +  𝑒−1,877  
= 13,16% 

 

By using the above formula, table 4 is obtained below. 

 

Table 4. Probability of traffic accident. 
 

Age Logit (p) 

Probability of being 

involved in the traffic 

accident 

20 -1.887 13.16% 

25 -1.577 17.12% 

26 -1.515 18.02% 

28 -1.391 19.92% 

30 -1.267 21.98% 

32 -1.143 24.18% 

33 -1.081 25.33% 

34 -1.019 26.52% 

35 -0.957 27.75% 

36 -0.895 29.01% 

38 -0.771 31.63% 

40 -0.647 34.37% 

41 -0.585 35.78% 

42 -0.523 37.22% 

43 -0.461 38.67% 

44 -0.399 40.16% 

45 -0.337 41.65% 

46 -0.275 43.17% 

47 -0.213 44.70% 

48 -0.151 46.23% 

50 -0.027 49.33% 

51 0.035 50.87% 

52 0.097 52.42% 

54 0.221 55.50% 



 

 

 

 
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Age Logit (p) 

Probability of being 

involved in the traffic 

accident 

55 0.283 57.03% 

56 0.345 58.54% 

58 0.469 61.51% 

60 0.593 64.41% 

 

Table 4 shows that the older the driver, the higher the probability of being involved in a traffic accident.  

3.3.3.  Logistic Regression Model Testing. To test the feasibility of the logistic regression model using 

the Hosmer and Lemeshow test (goodness of fit), assuming: 

H0: the model can explain the data 

H1: the model is unable to explain the data 

Test statistics: 

• H0 is accepted if Sig. > 0,05 or H1 is rejected if Sig. < 0,05 

• H0 is accepted if Chi-square count < Chi-square table 
 

Table 5. Hosmer and Lemeshow test for the logistic regression test for the age variable. 

Hosmer and Lemeshow Test 

Step Chi-square df Sig. 

1 3.641 7 .820 
 

 

In table 5 above, the value of Sig. = 0,820 > 0,05 and chi-square count = 3,641 < chi-square table = 

14,06741 so that H0 is accepted. With a 95% confidence level, it can be said that the logistic regression 

model used can explain data and deserves to be interpreted. 

 

Table 6. Model Summary for the logistic regression test for the variable of age. 

Model Summary 

Step 

-2 Log 

likelihood 

Cox & Snell R 

Square 

Nagelkerke R 

Square 

1 190.912a .060 .081 

a. Estimation terminated at iteration number 4 because 

parameter estimates changed by less than ,001. 
 

 

In table 6 above, the value of Nagelkerke R square = 0,081, so it can be said that the independent variable 

can explain the dependent variable by 8,1% so that other variables influence the rest by 100% - 8,1% = 

91,9%. 

 

4.   Conclusions 

The characteristics of the respondents of freight transport drivers on the Krian – Taman Sidoarjo road 

are dominated by the age of 26 to 45 years, about 56,7%, gender is male about 99,3%, the last education 

is Senior High School/ equivalent, about 64%, the drivers’ jobs about 65,3%, the average income per 

month is Rp. 2.500.000,- up to Rp. 5.000.000,- about 72,7%, types of freight transport vehicle is a 



 

 

 

 
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medium truck with open/closed/tank about 32,7%, vehicle load is in the form of general goods about 

50,0%, vehicle ownership status is the Company about 50% and the reasons for using the land mode to 

transport goods is more profitable about 50,7%. 

From the study results, it is known that the age variable affects the occurrence of traffic accidents by 

modelling: Logit (p) = -3,127 + 0,062 Age. The older the driver, the higher the probability of being 

involved in a traffic accident. 

Acknowledgments 

We thank you very much for the assistance, data, and survey support in the field to the Service Unit 

of Motor Vehicle Weighing Trosobo Sidoarjo and Police Resort Sidoarjo. 

 

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