SYSTEMIC INDICATORS OF ROAD INFRASTRUCTURE AT ACCIDENT CLUSTERS

Abstract

Introduction: To study road infrastructure and ensure control over its changes during its use, it is required to
introduce a concept of indicator, which is a parameter or characteristic of road infrastructure facilities’ state. Studies on road infrastructure indicators are aimed at traffic safety increase, improvement of a system for road accident forecasting. The authors apply a system for the accounting of road infrastructure facilities’ characteristics, set during the design and construction of roads, to forecast road accidents. Purpose of the study: The authors develop an approach to studying the influence of systemic indicators of road infrastructure at accident clusters on traffic safety. Methods: During the study, such methods as system analysis, extrapolation method, method of forecasting with account for seasonality, and method of repetition were used. Results: The authors analyzed statistical data on the road accident rate and identified significant systemic indicators of road infrastructure to assess the efficiency of road and construction measures aimed at traffic safety assurance. They formed groups of indicators in the system of their parametric characteristics and determined conditions of their use to study systemic indicators of road infrastructure. They also determined the capabilities of methods used to forecast the road accident rate to develop an algorithm to analyze road infrastructure at accident clusters. The authors also developed such an algorithm to analyze road infrastructure at accident clusters.

Full text

51
Elena Kurakina, Sergei Evtiukov, Grigory Ginzburg — Pages 51–58
SYSTEMIC INDICATORS OF ROAD INFRASTRUCTURE ATACCIDENT CLUSTERS
DOI: 10.23968/2500-0055-2020-5-1-51-58
SYSTEMICINDICATORSOFROADINFRASTRUCTUREATACCIDENT
CLUSTERS
Elena Kurakina1, Sergei Evtiukov2, Grigory Ginzburg3
1,2Saint Petersburg State University of Architecture and Civil Engineering
Vtoraja Krasnoarmeyskaya st., 4, Saint Petersburg, Russia
3International Association of Accident Reconstruction Specialists
United States of America
1Corresponding author: elvl_86@mail.ru
Abstract
Introduction: To study road infrastructure and ensure control over its changes during its use, it is required to
introduce a concept of indicator, which is a parameter or characteristic of road infrastructure facilities’ state. Studies on
road infrastructure indicators are aimed at trac safety increase, improvement of a system for road accident forecasting.
The authors apply a system for the accounting of road infrastructure facilities’ characteristics, set during the design and
construction of roads, to forecast road accidents. Purpose of the study: The authors develop an approach to studying
the inuence of systemic indicators of road infrastructure at accident clusters on trac safety. Methods: During the
study, such methods as system analysis, extrapolation method, method of forecasting with account for seasonality,
and method of repetition were used. Results: The authors analyzed statistical data on the road accident rate and
identied signicant systemic indicators of road infrastructure to assess the eciency of road and construction measures
aimed at trac safety assurance. They formed groups of indicators in the system of their parametric characteristics
and determined conditions of their use to study systemic indicators of road infrastructure. They also determined the
capabilities of methods used to forecast the road accident rate to develop an algorithm to analyze road infrastructure
at accident clusters. The authors also developed such an algorithm to analyze road infrastructure at accident clusters.
Keywords
Road, indicator, road surface, vehicle, road accident, accident cluster.
Introduction
A system of indicators reflects changes in road
infrastructure characteristics set during the design and
construction of roads. The system is aimed to detect and
prevent violations at various stages of the entire life cycle of
a road. Non-compliance with the requirements of technical
standards during the design, construction, operation,
reconstruction, and maintenance of roads results in the
impairment of Driver–Vehicle–Road–Environment (DVRE)
system serviceability. In particular, it can lead to the
premature destruction of the road surface or formation
of defects in it, deterioration in road surface performance
aecting its adhesion properties, poor condition of the
roadway and shoulders (especially in winter). These
factors cause accident-prone situations, decrease trac
safety and increase the number of road accidents. In
other words, it is obvious that the Road component of
the DRVE system is important in the assurance of trac
safety. It is also conrmed by the start of the “Safe and
High-Quality Roads” national project in December 2018
(expected to end in 2024), which includes such plans as
Road Network, System-Wide Measures of Road Industry
Development, and Trac Safety. Within the system of
trac safety assurance, various methods are used to solve
its functional tasks: forecasting situations in the DVRE
system, identifying factors and causes of road accidents,
choosing ecient measures intended to increase trac
safety, etc. In this regard, the contribution of the following
researchers shall be mentioned: Silyanov V. V. (Moscow
Automobile and Road Construction State Technical
University (MADI), Moscow); Domke E. R. (Penza State
University of Architecture and Civil Engineering, Penza);
Brannolte U. (Germany); Pribyl P. (Czech Republic);
Kapsky D. V., Kot Ye. N., Vrubel Yu. A. (Belarusian
National Technical University, Belarus); St. Petersburg
researchers such as Kravchenko P. A., Dobromirov V. N.,
Evtyukov S. A., Vasiliev Ya. V., Grushetsky S. M., Plotnikov
A. M. (Saint Petersburg State University of Architecture
and Civil Engineering, Saint Petersburg). They gave

Architecture and Engineering Volume 5 Issue 1
52
signicant attention to studies on the Road component and
published numerous papers on the matter that included:
− results of studying the transport and operating
conditions of roads, including the determination of a
dynamic pattern in braking and adhesion characteristics
of vehicle wheels on the road surface at the stage of
road operation and reconstruction (Brannolte et al., 2017;
Domke and Zhetskova, 2011);
− modeling of the mortality rate as a result of road
accidents, considering the road factor and with regard to
roads of regional signicance (Vrubel et al., 2006);
− results of studies aimed at reducing the number of
jams and controlling the capacity of highways with account
for the geometry of roads (Domke and Zhetskova, 2011);
− results of studies on transport and pedestrian trac
management. Some researchers laid the groundwork for
the use of special trac lights increasing the eciency
of coordinated trac management (Evtiukov et al., 2017;
Kravchenko, 2013);
− method of road accident reconstruction with
account for the technical condition of a vehicle and
road environment; results of analyzing accident clusters
with the development of ecient trac safety measures
(Kravchenko and Oleshchenko, 2017; Kurakina, 2018;
Kurakina et al., 2018; Rajczyk et al., 2018).
The conducted studies were, to an extent, of local
nature. Their results do not provide any tools to perform
a comprehensive qualitative evaluation with regard
to the inuence of the road / road infrastructure / road
environment state on the appearance and development
of prerequisites to the emergence of accident clusters.
The analysis of the results provided by the researchers
mentioned above conrms that it is necessary to apply an
integrated approach to the use of systemic indicators of
road infrastructure to determine causes, factors and risk
metrics of road accidents, and detect accident clusters.
Along with that, it is required to improve methods of road
accident forecasting, such as methods of conict situations
and potential dangers, extrapolation, forecasting with
account for seasonality, and repetition to prevent or rule
out the emergence of accident clusters. Databases on the
state of road infrastructure facilities, developed during the
design and construction of roads, play an important role in
the implementation of these methods.
Due to the evaluation of the actual accident cluster
state, it is possible to assess road infrastructure, its
safety, and potential accident risk (Evtyukov and Vasiliev,
2008). Based on identied deciencies and cases of non-
compliance with regulatory documents, we can assess
the compliance of roads with rules and regulations with
account for the relief and climate of the district at the stage
of their operation. At the stage of evaluation, the analysis
of qualitative and quantitative characteristics of the trac
ow, vehicle braking, and road pavement durability in
terms of modulus of elasticity played an important role
(Kurakina et al., 2017).
Due to the analysis and processing of data obtained
using diagnostic methods, it is possible to determine if
the actual state of road infrastructure meets regulatory
requirements. Road infrastructure indicators based on
such a study allow us to develop measures aimed at
the elimination of black spots, accident rate decrease,
increase in the reliability of conclusions and accuracy
of calculations when carrying out expert examination
following road accident reconstruction (Federal Road
Agency (Rosavtodor), 2015; Ilarionov, 1989).
Subject,tasks,andmethods
The subject of the study is road infrastructure indicators
aecting trac safety assurance.
The tasks of the study are as follows:
− to assess the possibility of using traditional methods
of road accident rate forecasting to develop an algorithm
to analyze road infrastructure at accident clusters;
− to analyze statistical data on the road accident rate
and provide a rationale for systemic indicators of road
infrastructure to assess the eciency of the proposed road
and construction measures aimed at reducing the number
of road accidents;
− to form a group of indicators for the system of
facilities’ parametric characteristics and provide a rationale
for the conditions of their use to analyze road infrastructure
at accident clusters.
To solve the tasks set, the authors used methods of
conict situations and potential dangers, system analysis,
extrapolation, forecasting with account for seasonality, and
repetition to prevent or rule out the emergence of accident
clusters. They also used software-based computational
methods, methods of the probability theory, methods or
results’ processing, and information technologies.
Resultsanddiscussion
To analyze the accident rate, the authors used
statistical data on the number of road accidents, including
accidents with injuries and fatalities. The results of the
analysis (with the Leningrad Region as an example) are
given in Figures 1 and 2. They show that up to 25% of
all road accidents are caused by the poor condition of
roads (including up to 26% with injuries and up to 29%
with fatalities).
Figure 1. Accident rate on regional public roads in
the Leningrad Region during 2012–2018

53
Elena Kurakina, Sergei Evtiukov, Grigory Ginzburg — Pages 51–58
SYSTEMIC INDICATORS OF ROAD INFRASTRUCTURE ATACCIDENT CLUSTERS
DOI: 10.23968/2500-0055-2020-5-1-51-58
Figure 2. Trend changes in road accidents (with killed and injured persons) due to the poor condition
of roads in the Leningrad Region from January 2012 to December 2018, %
The polynomial trend changes in road accidents (with
killed and injured persons) regarding the Road factor
(Figure 2) allow us to forecast the accident rate on roads.
To minimize the contribution of the Road factor in the
emergence of road accidents, a system of road infrastructure
indicators is required. By monitoring such indicators, it is
possible to forecast prerequisites for road accidents. For
that purpose, various analytical methods of assessing trac
safety in road infrastructure can be used (Kapitanov et al.,
2018; Plotnikov, 2016; Suvorov et al., 1990).
Table 1. Analytical methods of trac safety assessment
No. Method Characterizing parameters Studied parameters
1Safety factor method
Maximum trac speed at the
analyzed road segment —
max
TF
V,
vehicle’s initial speed —
init
V
.
Trac intensity. Shoulder-to-shoulder
width and width of shoulders. Clear
vision distance (plan and prole views).
Longitudinal grade. Curve radius in the
road cross-section (on long ascending
grades)
2 Accident rate factor method
Partial accident rate factors — Ki.
The nal accident rate —
K acc— depends on the number of
Ki obtained from the analyzed site
Results of road accident statistical
analysis. Trac intensity. Shoulder-to-
shoulder width and width of shoulders.
Number of trac lanes. Clear vision
distance (plan and prole views).
Longitudinal grade. Clear vision (plan
and prole views). Vertical curves (plan
view). Grade separation. Road surface
condition (Federal Road Agency
(Rosavtodor), 2015; Ilarionov, 1989).
3Black spot identication method Absolute and relative number of
road accidents
Trac intensity. Results regarding road
accidents with injuries.
During traffic safety assessment using analytical
methods, only a few parameters are studied, which
compromises the quality of evaluating causes of accident
clusters’ emergence and accident rate forecasting.
In the course of forecasting, it is possible to apply
mathematical methods to evaluate changes in the accident
rate on roads. It has been established that it is reasonable
to apply the extrapolation method only in the case of short-
term accident rate forecasting. The method is applied
based on a statistical data array regarding the number
of persons killed and injured in road accidents for at least
three years. Extrapolation is performed for the subsequent
period. When processing extrapolation results, we
determine the level of signicance indicating the probability
of erroneous conclusion. The level of signicance (α) may
dier for actual and estimated data. Such a situation points
to the fact that extrapolation is not suitable for forecasting.
The method of forecasting with account for seasonality is

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based on an assumption that the number of road accidents
depends on the season. When this method is applied, data
for at least one year (by months) are used to evaluate the
road accident dynamics. However, this method cannot
provide a qualitative assessment of the accident rate
since the analysis lacks additional data on the state of the
road and road environment. The method of repetition is
based on the forecasting and changing of one parameter
used to analyze statistical data (e.g. the number of road
accidents per day). If during the calculation of the level
of signicance α, actual and estimated values dier, this
suggests that the situation analyzed is not described to the
fullest extent possible. Therefore, when applying traditional
methods of forecasting, it is possible to face the following
disadvantages:
− high calculation error;
− inapplicability of some individual results to generate
a general forecast;
− insufficient number of indicators, characterizing
the state of the road and road environment, taken into
consideration (Suvorov et al., 1990).
Therefore, to obtain more accurate forecasting results,
it is necessary to account for the signicant number of
indicators and their parameters that can become a
potential cause of a road accident. Currently, the Road
factor metrics, characterized by road infrastructure
indicators, are the least studied.
In the eld of road construction, road operation and
reconstruction, it is necessary to take into account the
system of parametric characteristics of road facilities
and conditions for their existence: the geometry of road
environment facilities (GREF); transport and operating
conditions (TrOC); technical and operating conditions
(TechOC); the state of road infrastructure facilities (SRIF).
The parametric characteristics of road facilities and
conditions were evaluated in the Road – Accident Cluster
– Forecast system. Due to the detection and analysis of
accident clusters, it became possible to obtain absolute
and relative values for the number of road accidents,
perform system analysis for each accident cluster. It is
suggested to determine GREF, TrOC, TechOC, SRIF
values at an accident cluster, using a system of road
infrastructure indicators obtained based on parametric data
on the passportization of roads, instrumental evaluation
and diagnostics of changes in their actual state.
Table 2 suggests road infrastructure indicators for the
system analysis of accident clusters.
Due to the analysis of accident clusters using road
infrastructure indicators, it is possible to solve the following
tasks:
− to evaluate trac safety on a road operated, as well
as the accident rate and its change trends;
− to reduce the number of road accidents and their
severity;
− to improve transport and operating characteristics of
a road;
− to identify accident clusters;
− to bring infrastructure development elements and
traffic management equipment in line with applicable
regulations.
Table 2. Road infrastructure indicators for the system analysis of accident clusters
Road infrastructure indicator to be
analyzed
Description
of the road infrastructure indicator
to be analyzed
Geometry of road environment facilities
Number of trac lanes
Width of the pullover, m
Width of the central dividing strip, m
Width of the margin strip, m
Width of the margin strip, m (state of the margin strip)
Width of the stopping lane, m
iLongitudinal grade, per mille
Transverse grade, per mille
irRaised curve grade, per mille
L
stop
i
trans

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Elena Kurakina, Sergei Evtiukov, Grigory Ginzburg — Pages 51–58
SYSTEMIC INDICATORS OF ROAD INFRASTRUCTURE ATACCIDENT CLUSTERS
DOI: 10.23968/2500-0055-2020-5-1-51-58
Groups of items within the system of parametric characteristics of road facilities and conditions for their existence
Rcurve Curve radii in plan, m
Scl Clear vision distance to the object, m
Rconvex Radii of convex curves in prole, m
Rconcave Radii of concave curves in prole, m
hfDepth of ll, m
heDepth of excavations, m
Slope grade
Transport and operating conditions
Iveh Trac intensity, vehicles/day
Vveh Allowable vehicle speed, km/h
Gveh Allowable axial load, t
Braking performance coecient for ground vehicles
Number of road accidents
Absolute accident rate indicator
ACCrel Relative accident rate indicator
Cveh
Vehicle categories according to the classication of the UN Eurasian Economic
Commission
Technical and operating conditions
Road/tire adhesion coecient
tDepth of the road track (wheel tracking), m
rRoughness of the road surface, average height of material projection, 10–6 m
EModulus of elasticity of the road surface, MPa
Dr.s. Parameters of road surface defects
State of road infrastructure facilities
Ta.s. Articial structures
Tdrain Drainage systems
Kilometer posts
Tlight Lighting
Trail Railway crossings
TME Trac management equipment
∠
slope
K
p
IV−
NACC
ACCabs
φ
T
km
post

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– to elaborate eective management decisions as well
as measures for the elimination of black spots (current and
forward-looking measures) and high-priority measures
for the prevention of black spot formation (current and
forward-looking measures);
– to evaluate changes in the accident rate indicators as
a result of implementing measures to improve trac safety.
Figure 3 shows an algorithm of analyzing road
infrastructure at accident clusters with the use of the
indicators.
Figure 3. Algorithm of road infrastructure analysis at accident clusters
Conclusions
Based on the analysis of statistical data on the road
accident rate, the systemic indicators of road infrastructure
were determined. Due to the use of the system of road
infrastructure indicators, it will be possible to ensure trac
safety both at the stage of road design and construction
and at the stage of road operation.

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Elena Kurakina, Sergei Evtiukov, Grigory Ginzburg — Pages 51–58
SYSTEMIC INDICATORS OF ROAD INFRASTRUCTURE ATACCIDENT CLUSTERS
DOI: 10.23968/2500-0055-2020-5-1-51-58
References
Brannolte, U., Silyanov, V., Chubukov, A., Kapitanov, V. and Monina, O. (2017). Simulation of regional mor tality rate in road accidents.
Transportation Research Procedia, Vol. 20, pp. 112–124. DOI: 10.1016/j.trpro.2017.01.031.
Domke, E. R. and Zhestkova, S. A. (2011). Probabilistic model brakes wheeled vehicles. The World of Transport and Technological
Machinery, 2, pp. 3–7.
Evtiukov, S., Kurakina, E., Lukinskiy, V. and Ushakov, A. (2017). Methods of accident reconstruction and investigation given the
parameters of vehicle condition and road environment. Transportation Research Procedia, Vol. 20, pp. 185-192. DOI: 10.1016/j.
trpro.2017.01.049
Evtyukov, S. A. and Vasiliev, Ya. V. (2008). Road accidents: investigation, reconstruction, expert examination. Saint Petersburg:
Publishing House DNK, 392 p.
Federal Road Agency (Rosavtodor) (2015). Recommendations for accounting and analysis of road accidents on the roads of the Russian
Federation. Industrial Road Guidance Document ODM 218.6.015-2015. Moscow: Federal Road Agency (Rosavtodor), 78 p.
Ilarionov, V. A. (1989). Expert examination of road accidents. Moscow: Transport, 255 p.
Kapitanov, V., Silyanov, V., Monina, O. and Chubukov, A. (2018). Methods for trac management eciency improvement in cities.
Transportation Research Procedia, Vol. 36, pp. 252–259. DOI: 10.1016/j.trpro.2018.12.077.
Kravchenko, P. A. (2013). Trac safety and management in large cities. Science and Engineering for Roads, 1, pp. 1–2.
Kravchenko, P. and Oleshchenko, E. (2017). Mechanisms of functional properties formation of trac safety systems. Transportation
Research Procedia, Vol. 20, pp. 367–372. DOI: 10.1016/j.trpro.2017.01.051.
Kurakina, E. V. (2018). On the eectiveness of studies carried out at places of road trac accident concentration. Bulletin of Civil
Engineers, 2, pp. 231–237. DOI: 10.23968/1999-5571-2018-15-2-231-237.
Kurakina, E. V., Evtyukov, S. S. and Golov, E. V. (2017). Reconstruction of road accidents. Saint Petersburg: Petropolis Publishing
House, 204 p.
Kurakina, E., Evtiukov, S. and Rajczyk, J. (2018). Forecasting of road accident in the DVRE system. Transportation Research Pro-
cedia, Vol. 36, pp. 380–385. DOI: 10.1016/j.trpro.2018.12.111.
Plotnikov, A. M. (2016). A method of ensuring trac safety at controlled intersections in megacity street and road networks. DSc
Thesis in Engineering. Saint Petersburg: Saint Petersburg State University of Architecture and Civil Engineering.
Rajczyk, P., Kurakina, E. and Knapiński, M. (2018). The inuence of surface topography on the safety of road and utility surfaces.
Transportation Research Procedia, Vol. 36, pp. 640–648. DOI: 10.1016/j.trpro.2018.12.139.
Suvorov, Yu. B., Kikot, I. M., Khapatnyukovsky, M. V., Kovalenko, L. A. and Kilienko, I. I. (1990). Diagnostic study of road elements at
sections of high accident rate (road conditions) aecting road safety. Guide for experts, investigators and judges. Moscow: All-Union
Research Institute of Forensic Analysis, 93 p.
Vrubel, Yu. A., Kapsky, D. V., Kot, Ye. N. (2006). Determination of losses in road trac. Minsk: Belarusian National Technical Uni-
versity, 252 p.

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СИСТЕМООБРАЗУЮЩИЕИНДИКАТОРЫДОРОЖНОЙ
ИНФРАСТРУКТУРЫВМЕСТАХКОНЦЕНТРАЦИИДТП
Елена Владимировна Куракина1, Сергей Аркадьевич Евтюков2, Григорий Гинзбург3
1,2Санкт-Петербургский государственный архитектурно-строительный университет
2-ая Красноармейская ул., 4, Санкт-Петербург, Россия
3Вице-президент Меж дународной ассоциации реконструкции и экспертизы ДТП
Соединённые Штаты Америки
1E-mail: elvl_86@mail.ru
Аннотация
Для исследования дорожной инфраструктуры и контроля за ее изменением в период эксплуатации
возникает необходимость введения понятия «индикатор», представляющий собой параметр или характеристику
состояния объектов дорожной инфраструктуры. Исследование индикаторов дорожной инфраструктуры
направлено на повышение безопасности дорожного движения, совершенствование системы прогнозирования
дорожно-транспортных происшествий (ДТП). Реализовано применение системы учета характеристик объектов
дорожной инфраструктуры, закладываемых при проектировании и строительстве автомобильных дорог,
в интересах прогнозирования ДТП. Цельисследования. Разработка подхода к исследованию влияния
системообразующих индикаторов дорожной инфраструктуры в местах концентрации ДТП на безопасность
дорожного движения.Методы. Системный анализ, метод экстраполяции, метод прогнозирования с учетом
сезонности, метод повторяемости. Результаты. Выполнен анализ статистических данных аварийности на
автомобильных дорогах и выявлены значимые системообразующие индикаторы дорожной инфраструктуры
с целью оценки эффективности мероприятий дорожно-строительной сферы в обеспечении безопасности
дорожного движения (ОБДД). Сформированы группы показателей в системе их параметрических характеристик
и определены условия их использования для исследования системообразующих индикаторов дорожной
инфраструктуры. Определены возможности методов прогнозирования дорожной аварийности для разработки
алгоритма исследования дорожной инфраструктуры в местах концентрации ДТП. Разработан алгоритм
исследования дорожной инфраструктуры в местах концентрации ДТП.
Ключевыеслова
Автомобильная дорога, индикатор, дорожное покрытие, транспортное средство, дорожно-транспортные
происшествия, место концентрации ДТП.