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Transportation Research Procedia 63 (2022) 999–1006

X International Scientific Siberian Transport Forum

X International Scientific Siberian Transport Forum
Influence of the road surface roughness (according to the
Influence of the road surface roughness (according to the
International Roughness Index) on road safety
International
a,
Roughness
a
Index) on
a
road safety a
Egor Golova,*, Stanislav Evtyukova, Marina Protsutoa, Sergey Evtyukova, Elena
Egor Golov *, Stanislav Evtyukov Sorokina
, MarinaaProtsuto , Sergey Evtyukov , Elena
a
Sorokinaa
St. Petersburg State University of Architecture and Civil Engineering, 2nd Krasnoarmeyskaya st., 4, 190005, St. Petersburg, Russia
a
St. Petersburg State University of Architecture and Civil Engineering, 2nd Krasnoarmeyskaya st., 4, 190005, St. Petersburg, Russia

Abstract
Abstract
The high rate of road accidents and the various injuries associated with them are one of the most ambitious problems from an
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dependence.
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© 2022 The Authors. Published by ELSEVIER B.V.
© 2022
This is The
an Authors.
open access Published
article by ELSEVIER
under the CC B.V.
BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)
(https://creativecommons.org/licenses/by-nc-nd/4.0)
This is an open access article under the CC BY-NC-ND license
This
Peer-review under responsibility of the scientific committee of the (https://creativecommons.org/licenses/by-nc-nd/4.0)
is an
Peer-review open access
under article
responsibilityunder
of the CC BY-NC-ND
scientific license
committee of the X International
X International Scientific
Scientific Siberian
Siberian Transport
Transport Forum Forum
Peer-review
Keywords: under
Road responsibility
safety; of accident;
vehicle; traffic the scientific committee
International of theIndex;
Roughness X International
highway; IRI; Scientific
roughness;Siberian Transport Forum
road surface.
Keywords: Road safety; vehicle; traffic accident; International Roughness Index; highway; IRI; roughness; road surface.

1. Introduction
1. Introduction
Road traffic accidents cause enormous social, material and demographic damage to the Russian economy and
Roadastraffic
society accidents
a whole. cause enormous
In accordance social, material
with the Federal Law “Onand demographic
Road damage
Safety”, road safetytoisthe Russian as
understood economy
the stateand
of
society
this as a whole.
process In accordance
(the movement withand
of people the goods
Federalwith
Lawor“On Roadvehicles
without Safety”,within
road safety is understood
the road), reflecting as
thethe state of
degree of
this processof(the
protection movement of
its participants people
from and
traffic goods with
accidents and or without
their vehicles within
consequences. In turn,the road), reflecting
activities the degreethe
aimed at preventing of
protection of its traffic
causes of road participants from reducing
accidents, traffic accidents and their
the severity consequences.
of their In turn,
consequences, activitiesmore
is nothing aimed at preventing
than ensuring road the
causes of
safety, roadistraffic
which one ofaccidents, reducing
the priority areas the severity
of state of their
policy consequences,
and an is nothing
important factor more than
in ensuring ensuringsocio-
sustainable road
safety, which is one of the priority areas of state policy and an important factor in ensuring sustainable socio-

* Corresponding author. Tel.:+7-812-575-4261; fax:+7-812-575-4261.

* E-mail
Corresponding egorgoloff@yandex.ru
address:author. Tel.:+7-812-575-4261; fax:+7-812-575-4261.
E-mail address: egorgoloff@yandex.ru
2352-1465 © 2022 The Authors. Published by ELSEVIER B.V.
This is an open
2352-1465 access
© 2022 Thearticle under
Authors. the CC BY-NC-ND
Published by ELSEVIER B.V.(https://creativecommons.org/licenses/by-nc-nd/4.0)
license
Peer-review under
This is an open responsibility
access of the scientific
article under CC BY-NC-NDcommittee
license (https://creativecommons.org/licenses/by-nc-nd/4.0
of the X International Scientific Siberian Transport Forum )
Peer-review under responsibility of the scientific committee of the X International Scientific Siberian Transport Forum
2352-1465 © 2022 The Authors. Published by ELSEVIER B.V.
This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)
Peer-review under responsibility of the scientific committee of the X International Scientific Siberian Transport Forum
10.1016/j.trpro.2022.06.099
1000 Egor Golov et al. / Transportation Research Procedia 63 (2022) 999–1006
2 Egor Golov et al/ Transportation Research Procedia 00 (2022) 000–000

economic and demographic development of the country and aimed at preserving the life, health and property of
citizens of the Russian Federation.
Within the Russian Federation, relevant target programs of various levels are being developed and successfully
implemented. A landmark event in the road industry was the launch of the national project “Safe and High-Quality
Roads”, developed by the Ministry of Transport of Russia in pursuance of Decree of the President of the Russian
Federation dated May 7, 2018 No. 204 “On National Goals and Strategic Objectives for the Development of the
Russian Federation for the period up to 2024”. The project has become a logical continuation of the joint work of the
federal and regional departments of the road sector, the key goal of which is to improve the quality of life in the
population.
Within the framework of the national project “Safe and High-Quality Roads”, special attention is paid to the
quality of the roads themselves, as well as technical means of organizing traffic, which is reflected in one of the
goals of the Project, namely, bringing the road network of urban agglomerations to the standard state (in 2018 –
50%, in 2025 – 85%); reduction in the number of places of concentration of traffic accidents on the road network of
urban agglomerations (in 2018 – by 50% from the level of 2016, in 2025 – by 85%). The quality of roads and related
infrastructure depends on a large number of parameters. One of these is the operational indicators of the road
(Evtukov and others, 2018; Kurakina and others.2019; Petrov, 2021; Kurakina and others, 2020; Rajczyk and others,
2018; Golov, 2021; Evtukov and others, 2017; Evtukov and others, 2019; Kvitchuk and others, 2022).

2. Methods

A negative consequence of motorization is traffic accidents, one of the reasons for which is the insufficiently high
quality of road conditions. The vast majority of accidents are the result of not a single negative impact of a particular
factor, but their combination, which ultimately forms a traffic accident.
A special place in ensuring traffic safety belongs to road conditions, i.e. transport and operational characteristics
of roads. There are a large number of parameters that directly depend on the maintenance of roads and which, at a
low level, can cause a traffic accident or increase the likelihood of its occurrence. The most common of these are the
slipperiness of the roadway and the value of longitudinal roughness that does not meet regulatory requirements.
According to official statistics, deficiencies in the operational condition, arrangement of the road network and
railway crossings were recorded in 41,521 accidents, which accounted for a third (34%) of the total number of
registered accidents in 2021 (Fig. 1) – in these traffic situations 4,221 were killed and 51,882 were injured.

Fig. 1. Share of accidents with unsatisfactory road conditions of the total number of accidents.

The roughness of the road surface is one of the main indicators characterizing the convenience of driving on the
road and having a decisive influence on the speed of vehicles and the transportation function of the road as a whole.
In the modern world practice of monitoring the roughness of roads over the past 10–15 years, the International
Roughness Index (IRI) has become the most widely used. The unsatisfactory condition of the road surface is a
prerequisite for the emergence of a number of negative factors that adversely affect the conditions for the movement
of vehicles in the flow. Such is the vibration effect, which harms both the car and the driver. In addition, the working
conditions of the driver become much more complicated due to the fact that he is constantly forced to monitor the
road condition, the presence of potholes, pits, cracks on the way, while slowing down and picking up speed,
 Egor Golov et al. / Transportation Research Procedia 63 (2022) 999–1006 1001
Egor Golov et al / Transportation Research Procedia 00 (2022) 000–000 3

adjusting to the traffic situation and often changing the trajectory of movement. Such circumstances lead to the fact
that the driver's attention is concentrated on maneuvers, and not on other important components from the point of
view of ensuring traffic safety. Under such conditions, an increase in the accident rate is an inevitable phenomenon.
Fig. 2-5 are photographs of road sections with different indicators of surface roughness.

Fig. 2. Road section with IRI equal to 0-1 mm/m. Fig. 3. Road section with IRI equal to 1-2 mm/m.

Fig. 4. Road section with IRI equal to 2-4 mm/m. Fig. 5. Road section with IRI equal to 4-6 mm/m.

A general analysis of data on road traffic accidents shows that with the deterioration of the road surface
roughness, the number of road traffic accidents increases. Previous studies on the issues under consideration
establish a direct relationship between the roughness of the pavement and the number of accidents. However, it
should be noted that the increase in the number of accidents is not observed indefinitely, but up to a certain limit,
after reaching which there is a sharp decrease in the number of accidents, since the corresponding roughness
indicators are inherent in areas with an unsatisfactory operational condition, which forces vehicle drivers to
significantly reduce speed (Prashant and others, 2018; Heriberto and others, 2021; Gong and others, 2018;
Dobromirov and others, 2017).
In order to study the influence of the roadway roughness index on the level of road safety and the likelihood of an
accident, an analysis of the data on the roughness of federal roads of the Russian Federation (expressed in the
International Roughness Index (IRI), mm/m) was made, data on the intensity of traffic funds and data on recorded
road accidents were studied. The list of studied areas is presented in Table 1.

Table 1. Studied sections of federal highways.

No. Highway name Category Section, km+m – km+m

44+460 – 284+231
1 A-122 “A-114 - Ustyuzhna - Krestsy - Yazhelbitsy - Velikiye Luki – Nevel” II, III, IV
321+000 – 553+815
R-132 “Golden Ring” Yaroslavl - Kostroma - Ivanovo - Vladimir - Gus- 1086+425 – 1438+580
2 Khrustalny - Ryazan - Mikhailov - Tula - Kaluga - Vyazma - Rzhev - Tver - II, III, IV 608+471 – 698+300
Uglich - Yaroslavl 719+320 – 760+740
3 M-9 “Baltiya” Moscow - Volokolamsk - border with the Republic of Latvia III 158+862 – 419+700
4 M-10 “Russia” Moscow - Tver - Veliky Novgorod - St. Petersburg Iv, II 180+201 – 362+020
5 R-56 Veliky Novgorod - Soltsy - Porkhov - Pskov II, III 4+397 – 105+550
M-2 “Crimea” Moscow – Tula – Orel – Kursk - Belgorod - border with Ukraine 32+000 – 138+360
6 II
– Prokhorovka - Gubkin - R-298 Kursk - Voronezh - R-22 “Caspian” highway 138+360 –156+143
Belgorod - M-4 “Don” Moscow – Voronezh - Rostov-on-Don – Krasnodar – 8+000 – 216+100
7 II
Novorossiysk 216+100 – 319+275
0+000 – 10+141
8 M-2 “Crimea” Moscow - Tula - Orel - Kursk - Belgorod - border with Ukraine I
21+800 –108+000
5+983 – 54+540
A-103 “Shchelkovo highway” Moscow - Schelkovo - highway A-107 “Moscow
9 II 0+000 – 1+260
Small Ring”, Entrance to the city of Schelkovo, Entrance to Star City
0+000 – 4+700
1002 Egor Golov et al. / Transportation Research Procedia 63 (2022) 999–1006
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10 A-104 Moscow - Dmitrov - Dubna I, II, III 23+750 – 123+382

As part of the study, we used data on the roughness of the road surface obtained at the end of diagnostic work
(performed in accordance with the Road industrial methodical document ODM 218.4.039-2018), which were carried
out using mobile road complexes, including using infrared thermography methods (Fig. 6). Information processing
was carried out in specialized RDT-line software (Fig. 7).

Fig. 6. Visualization of roadway defects using infrared thermography.

Fig.7. Graph of the roughness index of the section of the M-2 “Crimea” highway: Moscow – Tula – Orel – Kursk - Belgorod - border with
Ukraine – Prokhorovka – Gubkin - R-298 Kursk - Voronezh - R-22 “Caspian” highway.

One of the main indicators that characterize the accident rate on roads is the risk of an accident, which implies a
quantitative expression of road accidents per 1 million car-km. To establish this value, a systematic analysis of the
available initial data for each studied road section was carried out, and a graph was constructed that reflects the trend
in the number of accidents depending on the roughness index in the corresponding section (Onayev and others,
2021; Abulizi and others, 2016).

3. Results

It was found that regardless of the number of lanes, the technical category of the road, the general nature of the
dependence under study is similar in each case under consideration. Examples of graphic dependence for highways
R-132 “Golden Ring” Yaroslavl - Kostroma - Ivanovo - Vladimir - Gus-Khrustalny - Ryazan - Mikhailov - Tula -
Kaluga - Vyazma - Rzhev - Tver - Uglich - Yaroslavl, A-122 “A-114 - Ustyuzhna - Kresttsy - Yazhelbitsy - Velikiye
Luki – Nevel”, M-9 “Baltiya” Moscow - Volokolamsk - border with the Republic of Latvia, Belgorod-M-4 “Don”
Moscow – Voronezh - Rostov-on-Don – Krasnodar - Novorossiysk, as well as the resulting graph are presented in
Fig.8-12. The nature of the change in the speed of the vehicle, depending on the regardless index is shown in Fig.13.
 Egor Golov et al. / Transportation Research Procedia 63 (2022) 999–1006 1003
Egor Golov et al / Transportation Research Procedia 00 (2022) 000–000 5

0,40 0,40
0,35 0,35
Accident risk indicator

Accident risk indicator

0,30 0,30
0,25 0,25
y = -0.0053x2 + 0.0542x + 0.2173 y = -0.0051x2 + 0.0513x + 0.2269
0,20 0,20
R² = 0.89 R² = 0.86
0,15 0,15
0,10 0,10
0,05 0,05
0,00 0,00
0,0 1,0 2,0 3,0 4,0 5,0 6,0 7,0 8,0 9,0 10,0 0,0 1,0 2,0 3,0 4,0 5,0 6,0 7,0 8,0 9,0 10,0
IRI, mm/m IRI, mm/m

Fig. 8. Dependence of the accident risk indicator on the road

surface roughness of the R-132 “Golden Ring” highway Fig. 9. Dependence of the accident risk indicator on the road
Yaroslavl - Kostroma - Ivanovo - Vladimir - Gus-Khrustalny - surface roughness of the А-122 highway “A-114 - Ustyuzhna
Ryazan - Mikhailov - Tula - Kaluga - Vyazma - Rzhev - Tver - - Krestsy - Yazhelbitsy - Velikiye Luki – Nevel”
Uglich - Yaroslavl

0,40 0,40
0,35 0,35
Accident risk indicator
0,30 0,30
Accident risk indicator

0,25 0,25
0,20 y = -0.0045x2 + 0.0467x + 0.2325 0,20
0,15 R² = 0.85 y = -0.0058x2 + 0.0555x + 0.2228
0,15
R² = 0.80
0,10 0,10
0,05 0,05
0,00 0,00
0,0 1,0 2,0 3,0 4,0 5,0 6,0 7,0 8,0 9,0 10,0 0,0 1,0 2,0 3,0 4,0 5,0 6,0 7,0 8,0 9,0 10,0
IRI, mm/m IRI, mm/m

Fig. 11. Dependence of the accident risk indicator on the road

Fig. 10. Dependence of the accident risk indicator on the road
surface roughness of the highway Belgorod-M-4 “Don”
surface roughness of the M-9 “Baltic” highway Moscow -
Moscow – Voronezh - Rostov-on-Don – Krasnodar -
Volokolamsk - border with the Republic of Latvia
Novorossiysk

100
0,40
0,35
80
0,30
Accident risk indicator

Speed, km/h

0,25 60
0,20 y = -0.0053x2 + 0.0527x + 0.224
0,15 R² = 0.90 40
0,10
0,05 20
0,00
0
0,0 1,0 2,0 3,0 4,0 5,0 6,0 7,0 8,0 9,0 10,0
0 1 2 3 4 5 6 7
IRI, mm/m
IRI, mm/m

Fig. 12. Dependence of the accident risk indicator on the road Fig. 13. Graph of the change in vehicle speed depending on
surface roughness of the sections of the considered federal the road surface roughness.
roads.

4. Discussion

Approximation of the dependence of the accident risk indicator on the longitudinal roughness of the road surface
(x) for roads of various types has the following expression:
1004 Egor Golov et al. / Transportation Research Procedia 63 (2022) 999–1006
6 Egor Golov et al/ Transportation Research Procedia 00 (2022) 000–000

y = - 0.0053x2 + 0.0527x + 0.224 (1)

The analysis of the obtained set of points made it possible to construct a trend line with a high degree of accuracy.
The approximation reliability value was 0.90, which indicates the reliability of the obtained approximation.
As can be seen from the graphs, when the IRI reaches the value of 5.0-5.5 mm/m, the risk of traffic accidents
decreases. This circumstance finds an explanation in the following: the peculiarity of the driver's perception of
damage on the road is such that a significant number or scale of damage forces a person to choose an appropriate
model of behavior on the road, i.e., to reduce speed in order to avoid damage to the vehicle or the potential
occurrence of an accident. Thus, the presence of a statistical relationship between the roughness of the road surface
and the likelihood of an accident is proved, which once again confirms the importance of this indicator for road
safety (Burtyl and others, 2021; Gladushevskiy, 2021; Kurakina and others, 2020; Kurakina, 2018; Piryonesi and
others, 2021; Alberti and others, 2017; Eboli and others, 2020).
The resulting analytical expressions can be used to assess and predict the level of road safety depending on
changes in the roughness of the road surface. An analysis of these dependencies shows that for the considered types
of roads in the studied range of changes in the international roughness index, the polynomial function with
approximation reliability coefficients R2 = 0.90 has the highest statistical convergence with experimental data,
which allows us to conclude that the description of the real distribution is highly accurate.
The accident probability indicator is calculated by the formula:

(2)

where A – number of accidents per 1 million car-km;

n – the number of accidents at the place of accidents’ concentration for the considered period;
N - average annual daily traffic intensity, cars/day;
L - length of the place of accidents’ concentration, km;
Т - number of years in the considered period (for motorways, highways, common roads of category I-III - 3 years,
category IV-V - 5 years).
To verify the obtained result, data on the roughness of the road surface on 775 public roads of regional and
intermunicipal significance in the Udmurt Republic with a total length of about 6 thousand km were analyzed for the
period from 2019 to 2021. In the course of the work, the results of calculating the risk indicator of road accidents in
places of concentration of road accidents in the Udmurt Republic in different years were compared according to the
known expression (2) and according to the obtained dependence (1). The result is presented in the form of an excerpt
from the materials of the study in Tables 2-4.

Table 2. Comparison of the results of calculating the risk indicator of road accidents in places of concentration of road accidents in the Udmurt
Republic for 2019 according to the known expression and according to the obtained dependence.

Actual value According to the study

No. Study section Accuracy, %
Accidents per 1 million car-km
1 “Izhevsk-Airport”, km 11-12 0.63 0.65 96
2 “Izhevsk-Votkinsk”, km 19-20 0.43 0.41 94
3 “Izhevsk-Votkinsk”, km 30-31 0.43 0.44 98
5 “Izhevsk-Votkinsk”, km 41-42 0.46 0.45 98

Table 3. Comparison of the results of calculating the risk indicator of road accidents in places of concentration of road accidents in the Udmurt
Republic for 2020 according to the known expression and according to the obtained dependence.

No. Study section Actual value According to the study Accuracy, %

1 “Izhevsk-Airport”, km 11-12 0.32 0.31 97
2 “Glazov-Yukamenskoe”, km 1-2 0.35 0.36 97
3 “(Zavyalovo - Golyany) - (Izhevsk - Airport)”, km 4-5 0.34 0.35 97
4 “Izhevsk-Votkinsk”, km 20-21 0.30 0.32 93
5 “Izhevsk-Uva”, km 12-13 0.31 0.32 96
6 “Izhevsk-Sarapul”, km 16-17 0.34 0.35 97
 Egor Golov et al. / Transportation Research Procedia 63 (2022) 999–1006 1005
Egor Golov et al / Transportation Research Procedia 00 (2022) 000–000 7

Table 4. Comparison of the results of calculating the risk indicator of road accidents in places of concentration of road accidents in the Udmurt
Republic for 2021 according to the known expression and according to the obtained dependence.

No. Study section Actual value According to the study Accuracy, %

1 “Igra – Glazov”, km 73-74 0.31 0.29 94
“Section of the Izhevsk bypass road from the Izhevsk-Airport
2 0.31 0.33 94
highway to the Izhevsk-Votkinsk highway", km 4-5
3 “Votkinsk bypass road”, km 9-10 0.4 0.32 82

The analysis of the dependencies presented in Figs. 8-13 made it possible to build a three-dimensional model that
displays the nature of the change in the two previously obtained functions in three-dimensional space (Fig. 14).

Fig. 14. A spatial model that characterizes the dependence of the accident risk and the car speed on the roughness of the road surface.

Based on the obtained data, it seems possible to determine the value of the longitudinal roughness of the road
surface, upon reaching which it is advisable to introduce a speed limit for vehicles on the corresponding section of
the road before the repair work is carried out by installing road signs 3.24 along its borders, which is clearly
demonstrated by the three-dimensional graph in Fig. 14.

5. Conclusions

Analyzing the obtained calculated data (tables 2-4), it can be stated that the calculation according to the proposed
method is sufficiently accurate and efficient and can be used to predict the level of road safety, as well as to prevent
the formation of dangerous sections on roads. It is advisable to use the results of this study for road balance holders
and operating organizations in the formation and approval of the schedule for the main work on the repair and
maintenance of roads, the implementation of preventive measures of various scales: low-cost (installation of signs,
applying horizontal and vertical markings), medium-cost (replacement of worn layers, sealing holes, cracks,
potholes, ruts), and large-cost (major repairs, reconstruction).

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