*Corr. Author’s Address: University of Ljubljana, Faculty of Mechanical Engineering, Askerceva 6, SI-1000 Ljubljana, Slovenia, jernej.klemenc@fs.uni-lj.si 455
Strojniški vestnik - Journal of Mechanical Engineering 69(2023)11-12, 455-470 Received for review: 2023-04-05
© 2023 The Authors. CC BY 4.0 Int. Licensee: SV-JME Received revised form: 2023-09-06
DOI:10.5545/sv-jme.2023.595 Original Scientific Paper Accepted for publication: 2023-09-15
Vehicle Technical Inspection Results in Relation  
to EU Directives and Selected EU Countries
Klemenc, J. – Šeruga, D. – Svetina, T. – Tršelič, J.
Jernej Klemenc
1,*
 – Domen Šeruga
1
 – Tomaž Svetina
2
 – Jože Tršelič
2
1 
University of Ljubljana, Faculty of Mechanical Engineering, Slovenia 
2 
Slovenian Traffic Safety Agency, Slovenia
Although road accidents caused by defective vehicles are relatively rare, the roadworthiness of vehicles is important for road safety. In the 
European Union, the technical conditions that need to be fulfilled by the vehicles using public roads are regulated by EU directives. According 
to these directives, technical inspections are organized in every EU country. Our study analyzed the vehicle technical inspections in Slovenia 
over four consecutive years. The statistics for passenger cars and commercial vehicles are compared with two other EU countries: Germany 
and Finland. From this comparison, it follows that certain differences exist between the countries, although vehicle technical inspections 
are regulated by the same EU directives. Finally, multivariate regression models were built to predict probabilities of various vehicle defects 
detected during regular technical supervision as a function of the vehicle’s age. It was found that the probability of fault detection can be 
effectively modelled as a function of vehicle age.
Keywords: passenger vehicles, commercial vehicles, technical supervision, fault statistics, statistical modelling
Highlights
• 	 A statistical analysis on technical supervisions was made for Slovenia.
• 	 Linkage between the technical state of vehicles and vehicle participating in road traffic accidents was evaluated for Slovenia.
• 	 Technical state of vehicles and typical detected defects were compared for Slovenia, Germany and Finland.
• 	 Uniformity of considering the EU directives that regulate the technical safety and roadworthiness was discussed.
0  INTRODUCTION
Every year, many serious traffic accidents occur, 
causing serious injuries or death to the people 
involved. There are many reasons for traffic accidents: 
human factor, inadequate infrastructure, technical 
defects of vehicles, and various combinations of the 
previously mentioned factors. Statistics from different 
countries around the world vary. In more developed 
countries, more traffic accidents are caused by human 
factors than by inadequate infrastructure or vehicle 
defects. In the USA, for example, vehicle defects are 
the main cause of accidents in 1.3 % to 2.7 % of cases 
[1], whereas this percentage is between 3 % and 6 % 
in the EU, according to Thomas et al. [2]. Rolison et 
al. [3] report that vehicle defects cause up to 12 % 
of accidents in the UK, but this information may be 
somewhat unreliable because it was obtained through 
a survey of police officers. In South Africa, the 
reported percentage of defect-related accidents varies 
between values of 2 % for direct causes and 6 % for 
indirect causes [4]. The most recent report for that 
country is 14.1 % for direct and indirect causes [5]. 
According to Van Schoore et al. [4], this percentage in 
less-developed African countries is higher and reaches 
16 %. In India, vehicle defects are responsible for 9 
% to 32 % of road traffic accidents, depending on the 
region [6]. Hudec and Sarkan [7] report that less than 1 
% of traffic accidents are caused by vehicle defects in 
Slovakia. For Slovenia, it was rather difficult to find 
this data, but Brcar [8] reports that vehicle defects are 
responsible for only 0.2 % of road traffic accidents. 
This figure is also questionable because in most non-
serious accidents the true cause of the accident is 
often not determined. Moreover, according to the non-
public data available for our study, the causes of traffic 
accidents are not reported in the police database. In a 
very recent systematic overview Martin-delosReyes et 
al. [9] stated that road crashed, which can be attributed 
to vehicle defects vary between 3 % and 19 % in 
developed countries and up to 27 % in developing 
countries.
Others investigated the causes of accidents in a 
narrower range; for example, Newman and Goode 
[10] report various influences on road accidents in 
Australia related to fleet management, maintenance, 
and use of commercial vehicles. Uchida et al. [11] 
investigated a relationship between the active safety 
components of the car and the main types of accidents 
in Japan. Garnowski and Manner [12] investigated the 
influence of the parameters of the connecting road to 
the highway on traffic accidents.
From the above-mentioned studies, it can be 
concluded that many different parameters, variables, 
and circumstances cause road accidents. However, 
when the main cause of accidents is a vehicle defect, 

Strojniški vestnik - Journal of Mechanical Engineering 69(2023)11-12, 455-470
456 Klemenc,	J.	–	Šeruga,	D.	–	Sv e tina,	T .	–	T ršelič,	J.
the following components have been identified as 
responsible in some selected countries:
• USA: poor condition or inadequate inflation of 
tyres (35 %), braking system defects (22 %), and 
steering system defects (3 %) [1], [13] and [14];
• UK: braking system defects, poor condition or 
inadequate inflation of tyres, steering-system 
defects, and poor lighting equipment [15];
• South Africa: poor condition or inadequate 
inflation of tyres (80 %), braking system defects 
(4 %), and steering system defects (3 %) [5].
Despite the small percentage of road accidents 
caused by defective vehicles, most of them could be 
prevented if all technical defects were detected during 
regular technical inspections. These inspections are 
required and enforced by the individual member 
states. In the EU, there are several directives that 
regulate the technical safety and roadworthiness of 
road vehicles:
• Council Directive 1999/37/EC on the registration 
documents for vehicles (document 01999L0037-
20180520) [16] and Directive 2014/46/EU 
amending the Council Directive 1999/37/EC on 
the registration documents for vehicles (document 
32014L0046) [17].
• Directive 2007/46/EC [18], a framework for the 
approval of motor vehicles and their trailers, and 
of systems, components, and separate technical 
units intended for such vehicles (document 
02007L0046-20200902). 
• Regulation (EC) No 661/2009 [19] concerning 
type-approval requirements for the general safety 
of motor vehicles, their trailers and systems, 
components, and separate technical units intended 
therefor (document 02009R0661-20190424).
• Directive 2014/45/EU [20] on periodic 
roadworthiness tests for motor vehicles and their 
trailers and repealing Directive 2009/40/EC 
(document 02014L0045-20140429).
Since the national regulations in the EU member 
states for road vehicle technical inspections are 
subordinate to the EU directives, it can be assumed 
that in the various member states with comparable 
vehicle populations, approximately the same statistics 
exist on the defects found during roadworthiness 
testing. Having obtained these statistical data for 
Slovenia, the data for passenger cars were compared 
with those of Finland and Germany. In addition, the 
data for commercial vehicles were compared with 
Germany. The reason for this is that reliable data 
sources in publicly available forms were found for 
these two EU countries.
Then, multivariate regression models were 
established to predict the probability of defect 
detection at technical inspections. Different static 
and dynamic regression models were tested for nine 
defect groups (lighting and electrical equipment, 
braking system, steering, vehicle identification, axles, 
chassis and suspension, visibility, emissions, and 
other equipment) for the Slovenian fleet of passenger 
cars, commercial vehicles, and motorcycles and 
ATVs. Finally, a correlation between the technical 
condition of vehicles and road accidents in Slovenia 
was investigated for the same three types of motor 
vehicles.
The article is organized as follows. After the 
introductory section, the basic statistical analyses and 
data models are explained. The third section presents 
the results and discussion. In this section, first, 
the process of data preparation is described. Then, 
the statistical data for Slovenia are presented and 
discussed, followed by a comparison with Germany 
and Finland. The section ends with regression models 
for predicting the probability of fault detection 
during technical inspections and a discussion of 
the relationship between the technical condition of 
vehicles and traffic accidents. The article ends with 
a concluding section, a list of references, and an 
acknowledgement.
1  METHODS
1.1  Data Cleaning and Pre-Processing
The following databases were obtained from the 
Slovenian Traffic Safety Agency and the Ministry of 
Internal Affairs:
• Databases of the vehicles’ roadworthiness that 
follow from the technical inspections for the 
years 2016 to 2022.
• Databases of defects detected during technical 
inspections for 2016 to 2022.
• Traffic accident databases for the years 2016, 
2017, and 2018.
The individual databases were composed of the 
following data:
• vehicle roadworthiness database (23 factors): 
vehicle ID, car brand, factory mark, commercial 
mark, commercial type, VIN number, designation 
of vehicle category, description of vehicle 
category, vehicle upgrade (label and description), 
additional equipment, type of fuel (label and 
description), date of the first vehicle registration, 
date of the first registration in Republic of 
Slovenia (RS), kilometres driven, vehicle owner 

Strojniški vestnik - Journal of Mechanical Engineering 69(2023)11-12, 455-470
457 Vehicle Technical Inspection Results in Relation to EU Directives and Selected EU Countries 
code, vehicle user code, technical inspection (TI) 
date, the outcome of the technical inspection 
(roadworthiness), the start of TI validity, end of 
TI validity, implementing contractor;
• vehicle defect database (16 factors): vehicle ID, 
VIN number, designation of vehicle category, 
description of vehicle category, vehicle upgrade 
(label and description), additional equipment, 
type of fuel (label and description), fault label, 
detailed fault description, item description (up to 
four items), vehicle condition assessment;
• traffic accident database (8 factors): vehicle 
ID, VIN number, licencing plate, car brand, 
commercial mark, vehicle mass, the maximum 
technical allowed mass of the vehicle, and date of 
the traffic accident.
For the statistical analyses, all three bases were 
linked via the VIN number of the vehicle. This was 
done using MS Access software by creating various 
queries that we used to facilitate data review, 
processing, and cleaning. For data processing and 
analysis, the vehicles from the databases were divided 
into five categories:
• passenger cars: categories M1 and M1G;
• buses: categories M2, M2G, M3, and M3G;
• commercial vehicles: categories N1, N1G, N2, 
N2G, N3, and N3G;
• motorcycles and ATVs: all categories with the 
designation L*;
• tractors: all categories with the designation T*.
For the most part, the vehicle roadworthiness 
database and the vehicle defects database clearly 
recorded the data, although there were some problems 
related to the letters with a canopy being written with 
different characters, indicating a mismatch in the text 
code tables. However, the problem was easily resolved 
by replacing these letters, as they were the same in all 
databases.
In addition to this deficiency, the following 
problems were the most common and troublesome in 
the individual records:
• Inconsistency of vehicle brand entries for older 
cars, trucks, and tractors (e.g., the numeral “0” is 
used instead of the letter “O”).
• The same vehicle models have different 
commercial marks (e.g., a “space” character is 
used where there should be no space numeral 
“0” is used instead of the letter “O”). Some 
commercial marks also contain the manufacturer’s 
name and/or more detailed information about the 
vehicle (e.g., engine description as 1.9 TDi).
• In some records, there is incorrect information 
about the commercial mark, or there is no 
such information at all. The inconsistency of 
commercial marks is high for motorcycles and 
tractors. Especially in the case of motorcycles, 
the number of brands is very large (>300) for 
two reasons: i.) inconsistent brand names and ii.) 
brands of which there is only one motorcycle in 
Slovenia (e.g., various niche manufacturers or 
local craftsmen). In addition, commercial-mark 
inconsistency is extremely high for commercial 
vehicles and tractors. The inconsistency of 
commercial marks for vehicles extends across all 
four years.
• The mileage (kilometres driven) is missing or 
incorrect (e.g., 0, 1, pure multiples of 10). The 
missing kilometres appear for vehicles that were 
not registered for the first time in the Republic 
of Slovenia and only for the first entry in the 
database. In later years, the kilometre data for 
these vehicles are entered.
• Duplicate records in the database for the same 
technical inspection result.
These problems and data inconsistencies were 
solved as follows:
• Additional data pre-processing with special 
macros in MS Access. In this way, most of the 
problems with passenger cars could be solved, 
but far fewer problems with the other four types 
of vehicles can.
• Manual correction of vehicle brands and 
commercial marks for some commercial vehicles, 
motorcycles, and tractors, which was very time-
consuming. Indeed, almost twice as much time 
was spent correcting the commercial marks of 
tractors and commercial vehicles for each year 
than for correcting the names of all passenger 
cars, even though the databases contained 
about ten times fewer records for tractors and 
commercial vehicles than for passenger cars.
• Regression of mileage based on vehicle age 
and statistical region. The main problem related 
to this inconsistency was a very large mileage 
spread. Therefore, this option was applied only to 
a limited extent so as not to affect the summary 
statistics too much.
• When processing statistical data on motorcycles, 
all brands for which only one technical inspection 
was carried out in the current year were excluded.
Table 1 shows the number of records for the 
performed technical inspections after the data 
cleaning was carried out for the individual years 
and individual types of motor vehicles. It can be 
concluded from Table 1 that the number of individual 
technical inspections of vehicles is consistent between 

Strojniški vestnik - Journal of Mechanical Engineering 69(2023)11-12, 455-470
458 Klemenc,	J.	–	Šeruga,	D.	–	Sv e tina,	T .	–	T ršelič,	J.
years, with a slight, but consistant, positive trend for 
passenger cars and commercial vehicles. 
1.2  Basic Statistical Analyses
For the five vehicle categories, the following statistical 
data were calculated:
• vehicle roadworthiness (share of acceptable, 
conditionally acceptable, or not acceptable 
vehicles and not acceptable vehicles with critical 
fault) as a function of the vehicle age summarized 
at the levels of regions and the whole of Slovenia;
• average mileage as a function of vehicle age 
summarized at the level of regions and Slovenia 
as a whole;
• absolute and relative numbers of the nine 
detected fault groups (according to the nine fault 
categories: lighting and electrical equipment, 
braking system, steering, vehicle identification, 
axles, chassis and suspension, visibility, 
emissions, and other equipment) as a function 
of the vehicle age summarized at the levels of 
regions and the whole of Slovenia;
• ranking of vehicle commercial marks according to 
their roadworthiness as a function of the vehicle 
age for passenger cars, commercial vehicles, and 
motorcycles;
• statistical distribution of the critical vehicle 
defects as a function of the vehicle age for 
commercial marks of passenger cars, commercial 
vehicles, and motorcycles.
In the last two cases, the statistical data for buses 
and tractors were not processed because the number of 
buses is too small, and the data on the roadworthiness 
of tractors are not reliable. In Slovenia, old tractors 
make up a significant part of the fleet. In the case 
of tractors, technical inspections do not have to be 
carried out at the site of the implementing contractors 
but can be done at an agreed-upon location in the 
farm’s field. However, the control of such technical 
inspection points is more difficult and complicated.
2.3  Multivariate Regression Models
In various fields, the linear regression method is one 
of the most widely used methods for modelling the 
dependence between independent and dependent 
variables. Let Y represent the dependent variable and  
X 

XX X
p 12
,, ..., vector of p independent variables. 
Then a multivariate linear regression model has the 
following form [21] and [22]:
 Ya aX
i
p
ii
 

 0
1
, (1)
a
0
 represents the constant term, and a
i
 represent the 
i
th
 regression coefficient. For the application of the 
multivariate linear regression models, the following 
conditions must be fulfilled [19]:
• a linear dependence exists between the Y and X
i
 
variables;
• variables X
i
 are mutually independent;
• prediction errors 
j j j
yy 

 are mutually 
independent and normally distributed with a 
mean value equal to zero and a constant variance.
y
j

 represent a predicted value of the dependent 
variable for the j
th
 sample in the set. The constant term 
and regression coefficients are free parameters of the 
multivariate linear regression model estimated by the 
least square method based on pairs of the independent 
and dependent variables x
jj
yj n ,; ,...,



1 . If 
the dependent and independent variables are 
standardized before building a multivariate linear 
regression model, then the regression coefficients of 
the standardized model represent the relative 
importance of the corresponding independent 
variables.
By using different transformations of the basic 
independent variables X
i
, multivariate regression 
can also be used to model non-linear dependencies 
between the independent and dependent variables. 
In this case, the condition of linear independence 
between each variable X
i
 is neglected. In our research, 
such a transformation was used to square some 
independent variables, which significantly improved 
Table 1.  Number of considered records for technical inspections of motor vehicles
Vehicle type
Year
2016 2017 2018 2019 2020 2021 2022
Passenger cars 99821 1029319 1040114 1062620 1052913 1069943 1077562
Commercial vehicles 118584 126839 135776 143332 145706 151155 153565
Motorcycles and ATVs 81382 103494 96187 99396 99747 105387 116107
Buses 5643 5876 6039 6105 5454 5514 5888
Tractors 103520 106124 107801 110001 105892 105387 111034

Strojniški vestnik - Journal of Mechanical Engineering 69(2023)11-12, 455-470
459 Vehicle Technical Inspection Results in Relation to EU Directives and Selected EU Countries 
the quality of the regression models for predicting the 
probability of defect detection.
Since we had at our disposal databases on 
vehicle technical inspections for seven consecutive 
years (2016 to 2022), the dynamic model of the 
dependence between the dependent (Y) and the 
independent variables (X
i
) was also built. They were 
set up either as simple regression models:
 Ya aX at
i
p
ii p
  

  0
1
1
, (2)
or as a decoupled time-space models with b
0
 and b
1
 
being the time-related regression coefficients:
 Ya aX bb t
i
p
ii
 
 
  

 0
1
01
. (3)
Eq. (3) can be rewritten into the following form:
 Ya aX at aXt
i
p
ii p
ip
p
ii
   




 0
1
1
2
21
. (4)
For all regression models, a coefficient of 
determination R
2
 was used to assess the quality of the 
regression model, because it represents a proportion 
of the data variance that is described by the regression 
model. The regression model is good if the value of 
R
2
 is close to one. IBM SPSS 26 and Microsoft Excel 
software were used to build the regression models.
Regression models for predicting the probability 
of defect detection in each of the consecutive four 
years are presented in Table 2. A total of 135 regression 
models were built for each year: (3 vehicle categories) 
× (5 different models) × (9 fault groups). Dynamic 
regression models for predicting the defect detection 
probability are presented in Table 3. Altogether, 54 
dynamic regression models were built: (3 vehicle 
categories) × (2 functional models) × (9 fault groups).
2  RESULTS AND DISCUSSION
2.1  Statistical Data for Slovenia
In Slovenia, different inspection periods are prescribed 
for different vehicles. For passenger cars, motorcycles, 
Table 2.  Basic regression models for individual years from 2016 to 2022
Model
Independent variables*
Passenger cars Commercial vehicles Motorcycles and ATVs
Model #1 X
1
 = vehicle age X
1
 = vehicle age X
1
 = vehicle age
Model #2
X
1
 = vehicle age
X
2
 = fraction of TI per age class
X
1
 = vehicle age
X
2
 = fraction of TI per age class
X
1
 = vehicle age
X
2
 = fraction of TI per age class
Model #3
X
1
 = vehicle age
X
2
 = X
1
 · X
1
X
1
 = vehicle age
X
2
 = X1 · X
1
X
1
 = vehicle age
X
2
 = X
1
 · X
1
Model #4 X
1
 = average mileage per age class
X1 = average mileage per age class
X
2
 = X
1
 · X
1
X1 = average mileage per age class
X
2
 = X
1
 · X
1
Model #5
X
1
 = average mileage per age class
X
2
 = fraction of TI per age class
X
1
 = average mileage per age class
X
2
 = fraction of TI per age class
X1 = average mileage per age class
X
2
 = fraction of TI per age class
*   The dependent variable Y in each regression model represents the defect detection probability at the technical inspection (TI). Therefore, each  
     regression model has 9 variations for 9 defect groups that are detected at TIs.
Table 3.  Dynamic regression models
Model
Independent variables*
,
**
Passenger cars Commercial vehicles Motorcycles and ATVs
Basic dynamic model
X
1
 = vehicle age
X
2
 = X
1
 · X
1
X
3
 = t = year
X
1
 = vehicle age
X
2
 = X
1
 · X
1
X
3
 = t = year
X
1
 = vehicle age
X
2
 = X
1
 · X
1
X
3
 = t = year
Decoupled dynamic model
X
1
 = vehicle age
X
2
 =X
1
 · X
1
X
3
 = t = year
X
4
 = vehicle age · year
X
5
 = X
2
 · year
X
1
 = vehicle age
X
2
 = X
1
 · X
1
X
3
 = t = year
X
4
 = vehicle age · year
X
5
 = X
2
 · year
X
1
 = vehicle age
X
2
 = X
1
 · X
1
X
3
 = t = year
X
4
 = vehicle age · year
X
5
 = X
2
 · year
*   The dependent variable Y in each regression model represents the defect detection probability at the technical inspection (TI). Therefore, each  
     regression model has 9 variations for 9 defect groups that are detected at TIs.
** The variable “year” represents the individual database, i.e., 2016 => X
3
 = 1; 2017 => X
3
 = 2 etc.

Strojniški vestnik - Journal of Mechanical Engineering 69(2023)11-12, 455-470
460 Klemenc,	J.	–	Šeruga,	D.	–	Sv e tina,	T .	–	T ršelič,	J.
and ATVs, the first technical inspection takes place at 
the age of four years. After that, technical inspections 
take place at the ages of six and eight years. After 
the vehicle is nine years old, technical inspections 
are performed every year. For commercial vehicles, 
buses, and tractors, the technical inspections take 
place every year. In this article, only the results for the 
entire country of Slovenia are presented to facilitate 
comparison with Germany and Finland.
For reasons previously explained, this article 
presents only the statistical data for passenger cars, 
commercial vehicles, motorcycles, and ATVs. For 
Fig. 1.  Results of the technical inspections: a) year 2018, b) year 2021; and average mileage: c) year 2018, d) year 2021; for passenger cars
Fig. 2.  Results of the technical inspections: a) year 2018, b) year 2021;  
and average mileage: c) year 2018, d) year 2021; for commercial vehicles

Strojniški vestnik - Journal of Mechanical Engineering 69(2023)11-12, 455-470
461 Vehicle Technical Inspection Results in Relation to EU Directives and Selected EU Countries 
passenger cars, the average mileage and the results of 
technical inspections in 2018 and 2021 are presented 
in Fig. 1. The same statistics are presented in Fig. 2 
for commercial vehicles and in Fig. 3 for motorcycles 
and ATVs. The statistics for the other five years are 
similar to these figures.
Fig. 3.  Results of the technical inspections: a) year 2018, b) year 2021;  
and average mileage: c) year 2018, d) year 2021; for motorcycles and ATVs
Fig. 4.  Detected defects as a function of the vehicle age – passenger cars: a) year 2018, b) year 2021

Strojniški vestnik - Journal of Mechanical Engineering 69(2023)11-12, 455-470
462 Klemenc,	J.	–	Šeruga,	D.	–	Sv e tina,	T .	–	T ršelič,	J.
Fig. 5.  Detected defects as a function of the vehicle age – commercial vehicles: a) year 2018, b) year 2021
Statistical data for 2016 to 2022 are consistent 
for the three vehicle categories. For passenger cars 
and commercial vehicles, the average mileage for 
technical inspections increases slightly but steadily 
from 2016 to 2022. The older the vehicle, the worse 
its technical inspection success and the higher its 
mileage.
After the age of 15 years, the decreasing trend of 
technical inspection success becomes a fairly constant. 
The trend in mileage as a function of age is rather 
proportional for motorcycles and A TVs, but degressive 
for passenger cars and commercial vehicles. It can 
also be seen in Figs. 2 and 3 that the peaks of technical 
inspections are shifted for three years, indicating a 
rather low volatility of the fleet. Since the number 
of technical inspections for commercial vehicles is 
rather small after the vehicle age of twenty years, 
it can be concluded that owners usually dispose of 
the worn-out vehicles. Except for passenger cars, 
the average mileage at technical inspection is not a 
good indicator of the technical roadworthiness of the 
vehicle. However, on average, passenger cars with 
lower mileage have slightly better technical inspection 
results than cars with higher mileage.
In this part of the study, a comparison was also 
made between the roadworthiness of vehicles found 
during technical inspections and the proportion of 
technically acceptable and unacceptable vehicles 
involved in traffic accidents. This comparison was 
made for passenger cars, commercial vehicles, and 
motorcycles and ATVs for the years 2017, 2018, 
and 2019. It was found that the relative proportion 
of acceptable/unacceptable vehicles in technical 
inspections corresponds very well with the records in 
the traffic accident database. Specifically, the vehicles 
that failed the technical inspection for the first time 
did not cause more accidents than the technically 
acceptable vehicles, as measured by their relative 
proportion in the technical inspections. This means 
that technical faultlessness is really not a major cause 
of road accidents in Slovenia.
Figs. 4 to 6 show distribution of detected 
defects according to vehicle age for passenger cars, 
commercial vehicles, motorcycles and ATVs for 2016 
and 2018. For the commercial vehicles, the statistics 
are shown for every two years.
In Figs. 4 to 6, it can be seen that the detected 
defects are consistent between the two years, which is 
also true for the other five years with an exception of 
the oldest vehicles. After the year 2020 the technical 
inspections became more strict for this age group, 
which results in an increased number of detected 
defects. The same is also true for the first regular 
technical inspection for motorcycles and ATVs (i.e. 

Strojniški vestnik - Journal of Mechanical Engineering 69(2023)11-12, 455-470
463 Vehicle Technical Inspection Results in Relation to EU Directives and Selected EU Countries 
at the age of four years). For passenger cars and 
commercial vehicles, the most likely deficiencies 
are lighting and electrical equipment, braking 
system, and other equipment (missing first aid kit for 
passenger cars and fire extinguisher for commercial 
vehicles). Motorcycles and ATVs are most likely 
to have deficiencies in lighting, electrical, and other 
equipment or fail the tests for Class L vehicles.
2.2  Comparison with Germany and Finland
For comparison with the other countries, only publicly 
available data on the Internet were used. Despite the 
fact that some recently research results on the vehicle 
technical inspections for different EU countries are 
published (e.g. see Hudec et al. [23] for all vehicles or 
Tapak et al. [24] for vehicles with smart technologies) 
it is difficult to find more detailed statistical data on 
age-dependent different types of vehicle failures. 
In most of the published literature a summarized 
statistical data on the vehicle defects can only be 
found. For this reason, the statistical data on individual 
defect types for Slovenia are compared only to the 
equivalent statistical data for Germany and Finland. 
For the three countries the available statistical data 
did not contain detailed information on the generic 
vehicle’s propulsion system (ICE, BEV or PHEV).  
For passenger cars, a Traficon database [24] 
was used for Finland and summarized TUeV reports 
[26] to [29] were used for Germany. For commercial 
vehicles, data from Verkehrs Rundschau [30] and 
[31] journal were used. Although the country-specific 
regulations for technical vehicle inspections are 
subordinate to the same EU directives, the main defect 
groups are subdivided differently into subgroups. For 
this reason, it was difficult to compare the statistical 
data between EU countries. To make the comparison 
possible, the statistical data from the defect subgroups 
of Finland and Germany were combined so that the 
data corresponded to the Slovenian defect groups.
Table 4 compares the roadworthiness of passenger 
cars in Germany and Slovenia. Table 5 compares the 
probabilities of detecting typical defects in passenger 
cars in Germany and Slovenia. Fig. 7 compares the 
probabilities of defect detections for passenger cars in 
Finland and Slovenia. A comparison between Finland 
and Slovenia is made only for 2017 to 2019, since data 
on the technical inspections of passenger cars were 
not available for Finland for 2016. Fig. 8 shows the 
roadworthiness of commercial vehicles in Germany. 
Finally, Fig. 9 compares the probabilities of defect 
detections for typical commercial vehicle defects 
between Germany and Slovenia. This comparison for 
is made only for five years due to data availability for 
Germany.
Fig. 6.  Detected defects as a function of the vehicle age – motorcycles and ATVs: a) year 2018, b) year 2021

Strojniški vestnik - Journal of Mechanical Engineering 69(2023)11-12, 455-470
464 Klemenc,	J.	–	Šeruga,	D.	–	Sv e tina,	T .	–	T ršelič,	J.
Fig. 7.  Defect detection probabilities for passenger cars in Finland and Slovenia: a) lighting and electrical equipment, b) braking system,  
c) steering equipment, d) vehicle identification, e) axles, wheels and suspension, f) chassis and body, g) emissions, h) visibility
From Tables 4 and 5, and Fig. 7, the following 
can be concluded for passenger cars:
• The stringency of technical inspections is higher 
in Germany for all age classes of passenger cars 
than in Slovenia, where far fewer vehicles are 

Strojniški vestnik - Journal of Mechanical Engineering 69(2023)11-12, 455-470
465 Vehicle Technical Inspection Results in Relation to EU Directives and Selected EU Countries 
considered to be in perfect condition. A direct 
comparison of the defects detected during 
technical inspections is only possible to a limited 
extent due to the different specifications of the 
defects in Germany and Slovenia. Nevertheless, 
it can be stated that the proportions of the most 
common defects (i.e., lighting system and brake 
system) are similar in Slovenia and Germany.
• In contrast to Germany, it is difficult to claim 
that technical inspections in Finland are stricter 
than in Slovenia. For four defect groups (lighting 
and electrical equipment; axles, wheels and 
suspension; chassis and body; visibility), more 
defects are found in Finland than in Slovenia, 
while for three defect groups (braking system; 
steering; emissions), more defects are found in 
Slovenia. No significant differences were found 
in vehicle identification. In both countries, the 
most frequent defects are found in the braking 
system and in lighting and electrical equipment.
Fig. 8.  Roadworthiness of commercial vehicles in Germany at technical inspections: a) years 2016/2017, b) years 2019/2020
Fig. 9.  Defect detection probabilities for 1 to 5 years old commercial vehicles in Germany and Slovenia:  
a) lighting equipment, b) braking system, c) chassis and suspension, d) emissions, e) visibility

Strojniški vestnik - Journal of Mechanical Engineering 69(2023)11-12, 455-470
466 Klemenc,	J.	–	Šeruga,	D.	–	Sv e tina,	T .	–	T ršelič,	J.
Table 4.  Roadworthiness of passenger cars in Germany and 
Slovenia at technical inspections
Age of the techni-
cally unacceptable 
vehicles
Germany
[%]
Slovenia – all technically  
unacceptable vehicles* [%]
2016 2017 2018 2019
4 to 5 10.3 1.9 1.7 1.7 1.7
6 to 7 16.3 3.4 3.7 3.4 3.3
8 to 9 22.2 4.6 4.8 4.8 4.6
10 to 11 28.0 6.3 6.4 6.0 6.4
* The first technical inspection for passenger cars is carried out at 
   the age of four years in Slovenia
Table 5.  Probabilities of fault detections for passenger cars in 
Germany and Slovenia
Fault type Germany
Slovenia
2016 2017 2018 2019
Lighting equipment 7.3 % 10.7 % 11.9 % 11.4 % 10.5 %
Suspension elements 4.5 % 0.3 % 0.3 % 0.3 % 0.4 %
Braking system 2.8 % 3.3 % 3.3 % 2.9 % 2.1 %
From Figs. 2, 8, and 9, the following conclusions 
can be drawn for commercial vehicles:
• The average mileage of commercial vehicles 
up to five years old is comparable in Slovenia 
and Germany, but the percentage of defect-free 
vehicles (including critical defects) is lower in 
Slovenia than in Germany for the same vehicle 
age.
• The relative frequency of detected defects is not 
the same in Slovenia and Germany, although it 
is difficult to compare these data because of the 
different specifications of the defects. Never the 
less, no significant differences were found for the 
lighting equipment, brake system, visibility, or 
chassis and suspension. In both countries, the most 
common defects are with the lighting system.
From the comparisons presented, it can be 
concluded that technical inspections in the three 
countries place a different emphasis on individual 
defect groups. As a result, the roadworthy condition 
of vehicles may be assessed differently in these 
countries. Nevertheless, the most common defects 
found in these countries are the same, which means 
that the prescribed technical inspection procedures do 
not differ to an unacceptable extent.
2.3  Regression Models for Predicting the Probability of 
Fault Detection
Figs. 10 and 11 show the defect detection probabilities 
at technical inspections in the years 2018 and 2021 
for commercial vehicles, motorcycles and ATVs. The 
statistics for the other five years are similar and will 
not be presented here. For the passenger cars, these 
probabilities are shown in Fig. 7 for the years 2017, 
2019 and 2021. The statistics for the other years are 
also similar. When these figures are compared to Fig. 
4 to 6 the same trend can be noticed, i.e., that after the 
year 2020 the technical inspections became more strict. 
Since more faults were detected at approximately the 
same number of technical inspections per year the 
probability of fault detection rose in the years 2020 
to 2022.
From Figs. 7, 10, and 11, it can be seen that 
the probability of defect detection during technical 
inspections did not increase proportionally with 
vehicle age. It was also found that vehicle age 
predicted the probability of defect detection much 
better than average mileage. Consequently, the basic 
regression model #3 from Table 2 was consistently 
found to be the best for the three vehicle categories 
and defect groups studied. The regression coefficients 
and the values of the deterministic coefficient R
2
 for 
Fig. 10.  Defect detection probabilities for commercial vehicles in Slovenia: a) year 2018, b) year 2021

Strojniški vestnik - Journal of Mechanical Engineering 69(2023)11-12, 455-470
467 Vehicle Technical Inspection Results in Relation to EU Directives and Selected EU Countries 
the basic regression model #3 are shown in Table A1 
in Appendix for the relevant fault groups and the year 
2018. Since the coefficients for the other six years are 
very similar, they are not shown here. Both dynamic 
regression models from Table 3 were also successful 
in predicting the probability of defect detection, but 
the decoupled model was consistently better than the 
basic dynamic model. For this reason, Table A2 in 
Appendix presents the regression coefficients and the 
values of the deterministic coefficient R
2
 for only the 
decoupled dynamic regression models for the relevant 
defect groups.
From Table A1, we can conclude that the 
regression models for passenger cars and commercial 
vehicles are good, as reflected in high values (R
2
 > 0.7) 
of the R
2
 value for all error groups except emissions 
and vehicle identification. In contrast, the regression 
models for motorcycles and ATVs are poor. This 
results from the following facts: i) the probabilities of 
fault detection are much lower compared to passenger 
cars and commercial vehicles, and ii) the probabilities 
of fault detection vary significantly between different 
vehicle ages. 
Comparing Table A2 with Table A1, we see that 
the dynamic regression models are comparable the 
base regression model #3 according to their R
2
 values 
remain relatively high (R
2
 > 0.7) for passenger cars 
and commercial vehicles. The dynamic regression 
models for the motorcycles and ATVs are poor for 
the same reason explained earlier. Never the less, it 
is advantageous to model the probability of fault 
detection separately for each year, because it can be 
concluded from the results, which were presented 
before, that there are relatively more faults detected at 
technical inspections after the year 2020.
3  CONCLUSIONS
In the EU, several directives regulate the technical 
safety and roadworthiness of road vehicles. 
Moreover, national regulations are subordinate to 
these directives. In our study, the roadworthiness of 
vehicles found during regular technical inspections 
was statistically analysed and compared with the 
road accident database. In this part of the study, it 
was found that the technically unsound vehicles (i.e., 
vehicles that did not pass the technical inspection for 
the first time) do not contribute more to road accidents 
than the technically roadworthy vehicles, as measured 
by their relative share determined during the technical 
inspections.
Then, the statistics for passenger cars and 
commercial vehicles were compared with those of the 
other two EU countries (i.e., Germany and Finland). 
From this comparison, it can be concluded that the 
individual defect groups are weighted differently 
in the different countries. This could lead to some 
differences in roadworthiness ratings across EU 
countries. Nevertheless, the most frequently identified 
defects were the same in the countries compared, 
which means that the prescribed technical inspection 
procedures do not differ to an unacceptable extent.
To complement this, year-specific and dynamic 
regression models were also constructed to predict the 
probability of defect detection for Slovenian passenger 
cars, commercial vehicles, motorcycles, and ATVs. It 
was found that the probability of defect identification 
during technical inspection can be predicted well 
for almost all defect groups for passenger cars and 
commercial vehicles. However, due to data variation 
and inconsistency, this cannot be reliably done for 
motorcycles and ATVs. Dynamic regression models 
Fig. 11.  Defect detection probabilities for motorcycles and ATVs in Slovenia: a) year 2018, b) year 2021

Strojniški vestnik - Journal of Mechanical Engineering 69(2023)11-12, 455-470
468 Klemenc,	J.	–	Šeruga,	D.	–	Sv e tina,	T .	–	T ršelič,	J.
were also found to have somewhat lower modelling 
power than the year-specific base regression models.
4  ACKNOWLEDGEMENTS
The authors acknowledge financial support from the 
Slovenian Research Agency and Slovenian Traffic 
Safety Agency (CRP project No. V2-1929 entitled 
“Analiza napak na vozilih ugotovljenih pri postopkih 
tehničnih pregledov vozil s konvencionalnimi 
statističnimi metodami in z metodami rudarjenja 
podatkov” / “Analysis of vehicle defects identified at 
technical supervisions using conventional statistical 
methods and data mining”).
5  NOMENCLATURES
a
0 
constant term of a linear regression model, [-]
a
i 
linear regression coefficient of the i
th
 term, [-]
b
0 
time-related constant term in the dynamic 
regression model, [-]
b
1 
time-related linear coefficient in the dynamic 
regression model, [-]
n number of sample points, [-]
p number of independent random variables, [-]
t time [s]
x
i 
realization of the i
th
 independent random variable 
X
i
, [-]
x realization of the independent vector X, [-]
X
i 
i
th
 independent random variable, [-]
X vector of independent random variables, [-]
y realization of the dependent random variable Y, 
[-]
Y dependent random variable, [-]
ε	 prediction error, [-]
j running index, [-]
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7  APPENDIX
Table A1.  Regression coefficients and determination coefficients for the basic regression model #3 for the year 2018 
Coeffi-
cients
Light. & 
elect.
Brake 
system
Steering
Other 
equip.
Vehicle 
ident.
Cat. L tests Axles
Chassis & 
susp.
Visibility Emissions
Passenger cars
a
0
-0.0793 -0.0336 -0.0085 -0.0078 0.0000 n/a -0.0170 -0.0040 -0.0091 -0.0091
a
1
0.0319 0.0106 0.0033 0.0062 0.0002 n/a 0.0061 0.0007 0.0034 0.0017
a
2
-0.0010 -0.0002 -0.0001 -0.0002 0.0000 n/a -0.0002 0.0001 -0.0001 0.0000
R
2
0.9245 0.9079 0.9064 0.7764 0.8657 n/a 0.8772 0.8520 0.8868 0.6826
Commercial vehicles
a
0
-0.0487 -0.0516 -0.0098 -0.0059 -0.0032 n/a -0.0225 -0.0204 -0.0042 -0.0039
a
1
0.0362 0.0213 0.0059 0.0188 0.0016 n/a 0.0103 0.0069 0.0043 0.0014
a
2
-0.0011 -0.0005 -0.0002 -0.0005 -0.0001 n/a -0.0003 -0.0001 -0.0001 0.0000
R
2
0.9662 0.9218 0.8258 0.8854 0.6144 n/a 0.8879 0.7942 0.8873 0.5675
Motorcycles and ATVs
a
0
0.0012 -0.0018 -0.0002 0.0014 0.0005 0.0174 -0.0004 0.0000 0.0022 -0.0002
a
1
0.0021 0.0007 0.0001 0.0014 0.0000 -0.0002 0.0006 0.0001 0.0000 0.0001
a
2
-0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
R
2
0.5816 0.2884 0.2038 0.5436 0.1875 0.0048 0.3602 0.1377 0.0787 0.1805

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470 Klemenc,	J.	–	Šeruga,	D.	–	Sv e tina,	T .	–	T ršelič,	J.
Table A2.  Regression coefficients and determination coefficients for the two dynamic regression models
Coeffi-
cients
Light. & 
elect.
Brak. 
system
Steering
Other 
equip.
Vehicle 
ident.
Cat. L tests Axles
Chassis & 
susp.
Visibility Emissions
Passenger cars
a
0
-0.0352 -0.0368 -0.0071 0.0039 -0.0015 n/a -0.0100 -0.0057 -0.0073 -0.0205
a
1
0.0235 0.0123 0.0031 0.0052 0.0002 n/a 0.0040 0.0011 0.0031 0.0050
a
2
-0.0008 -0.0002 -0.0001 -0.0002 0.0000 n/a -0.0001 0.0000 -0.0001 -0.0001
a
3
-0.0112 0.0028 -0.0006 -0.0012 0.0008 n/a -0.0019 0.0009 0.0003 0.0036
a
4
0.0028 -0.0008 0.0002 0.0000 0.0000 n/a 0.0010 -0.0002 0.0001 -0.0010
a
5
0.0000 0.0000 0.0000 0.0000 0.0000 n/a 0.0000 0.0000 0.0000 0.0000
R
2
0.9305 0.9412 0.9214 0.8295 0.8313 n/a 0.9185 0.9497 0.9194 0.8710
Commercial vehicles
a
0
-0.0138 -0.0191 -0.0066 0.0377 -0.0027 n/a -0.0144 -0.0133 -0.0012 -0.0045
a
1
0.0227 0.0137 0.0043 0.0150 0.0004 n/a 0.0070 0.0039 0.0029 0.0013
a
2
-0.0007 -0.0001 -0.0001 -0.0004 0.0000 n/a -0.0002 -0.0001 0.0000 0.0000
a
3
-0.0035 -0.0022 -0.0010 -0.0015 0.0005 n/a -0.0009 -0.0002 0.0004 -0.0002
a
4
0.0053 0.0009 0.0010 0.0006 0.0005 n/a 0.0014 0.0009 0.0006 0.0002
a
5
-0.0002 0.0000 0.0000 0.0000 0.0000 n/a 0.0000 0.0000 0.0000 0.0000
R
2
0.9570 0.9149 0.8673 0.8534 0.8447 n/a 0.8932 0.9213 0.9129 0.8722
Motorcycles and ATVs
a
0
0.0076 -0.0016 0.0001 0.0060 0.0001 0.0101 0.0015 0.0001 0.0044 -0.0003
a
1
0.0011 0.0008 0.0000 0.0016 -0.0002 -0.0007 0.0004 0.0001 -0.0002 0.0001
a
2
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
a
3
0.0002 0.0004 0.0001 -0.0004 0.0014 0.0077 -0.0001 0.0003 -0.0001 0.0001
a
4
0.0000 0.0000 0.0000 0.0001 0.0001 0.0000 0.0000 0.0001 0.0001 0.0000
a
5
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
R
2
0.1514 0.1234 0.0961 0.1299 0.1710 0.3591 0.1128 0.1243 0.1102 0.0916