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THE MAIN CAUSES AND ANALYSIS OF INJURIES THAT OCCUR IN CAR
ACCIDENTS
Ergashev Muhammadrashidkhan Ilhomjonovich
Doctoral student. Teacher, Fergana State Technical University,
Uzbekistan, Fergana region, Fergana
Orcid: https://orcid.org/0009-0004-1822-6769
E-mail: m.rasheedkhan97@gmail.com
Luqmonjanov Asadbek Bahadirjonovich
Student, Fergana State Technical University,
Uzbekistan, Fergana region, Fergana
E-mail: asadbekluqmonjonov19@gmail.com
Orcid: https://orcid.org/0009-0001-0246-8622
https://doi.org/10.5281/zenodo.15738077
ОСНОВНЫЕ ПРИЧИНЫ И АНАЛИЗ ТРАВМ, ВОЗНИКАЮЩИХ ПРИ
АВТОМОБИЛЬНЫХ АВАРИЯХ
Эргашев Мухаммадрашидхан Илхомжонович
Докторант.Преподаватель, Ферганского политехнического института,
Узбекистан, Ферганская область, Фергана
Лукмонжанов Асадбек Бахадиржонович
Студент, Ферганский политехнический институт,
Узбекистан, Фергана область, Фергана
ABSTRACT
One of the factors that increase the risk of deaths and accidents in the road transport
system is the intersection, which is the main network connecting the movement of drivers and
pedestrians. At intersections, people collect and analyze data through traditional methods of
assessing life safety. Due to the conventional analysis, the design comparison of intersections
in the road system formed at the center of the crash is quite complicated and not easily applied.
The purpose of this study was to examine the relationship between crash injuries, injury rates,
environment, time, and driving conditions using the CRIS database. Studies show that the
number of injuries and injuries in existing road traffic accidents at the intersection is higher
than the number of collisions, and the number of motor vehicle accidents increases between
12:00 and 15:00. In addition, the highest number of injuries as a result of collisions was
observed in the autumn season, the lowest number of injuries was found in the winter season,
and men suffered more road traffic accidents than women.
АННОТАЦИЯ
Одним из факторов, повышающих риск гибели людей и аварий в дорожно-
транспортной системе, является перекресток, который является основной сетью,
соединяющей движение водителей и пешеходов. На перекрестках люди собирают и
анализируют данные с помощью традиционных методов оценки безопасности жизни.
Из-за традиционного анализа сравнение конструкций перекрестков в дорожной
системе, образовавшейся в центре аварии, достаточно сложно и нелегко применимо.
Целью данного исследования было изучение взаимосвязи между травмами в результате
аварий, уровнем травматизма, окружающей средой, временем и условиями вождения с
использованием базы данных CRIS. Исследования показывают, что количество травм и
травм в существующих дорожно-транспортных происшествиях на перекрестке
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превышает количество столкновений, а количество дорожно-транспортных
происшествий увеличивается в период с 12:00 до 15:00. При этом наибольшее
количество травм в результате столкновений наблюдалось в осенний сезон,
наименьшее количество травм - в зимний сезон, а мужчины пострадали в дорожно-
транспортных происшествиях больше, чем женщины.
Keywords:
intersection, environment, driver, chi-square, number of injuries, CRIS
Ключевые слова:
перекрёсток, окружающая среда, водитель, хи-квадрат,
количество пострадавших, CRIS
Introduction.
The intersection is important for road users and is a hotbed of car accidents. It is
important to develop a structure of counter-intersections to overcome this problem, maintain
traffic safety, and prevent high-risk accidents and collisions. Car accidents occurred mainly at
the intersections of city signs, and relatively few serious accidents occurred. Also, male drivers
make dangerous maneuvers late at night, causing violations and traffic jams. In addition, it has
been observed that the level of danger sharply increases at pedestrian crossings, which is
intended for traffic management. Emphasize funding for dangerous intersections, separating
bike lanes and sidewalks from the roadway, improving lighting, increasing evening law
enforcement activity, and stop-and-signal intersections as traffic control it is recommended to
introduce directions. To ensure the safety of their movement, drivers and pedestrians must
make conscious decisions at pedestrian crossings and intersections. This central location
creates complexity and increases the level of danger to maintain the safety of movement
between them. One of the main causes of this situation is drivers who drink alcohol and ignore
traffic lights, especially at night, because they have a very late decision-making time.
Intersections are a complex problem in every country, and their safety cannot be ensured only
by traffic lights and signs. To solve this problem, it can be achieved through a national
comprehensive program on improving the safety of road users and managing vehicle
intersections. First, we need to correctly identify intersections with a high risk of collisions and
serious injuries. It is also necessary to take into account the weather data at the time of the
accident at the intersections, because the weather accounts for 90% of the accidents, which
means that it is a situation beyond the control of the driver and the pedestrian. Data from 3 to
5 years is relatively sufficient for accident analysis. For this reason, short-term statistics may
not give us an accurate analysis. Traffic simulation is used to assess the level of safety related
to the risk of traffic accidents at an intersection.
Literature analysis.
The role of the intersection in ensuring road traffic safety is high, and it provides informal
communication between the pedestrian and the driver. However, the number of accidents and
injuries is high at city intersections, especially at night when the driver consumes alcohol. Also,
the number of accidents at intersections accounts for a significant proportion of all road-related
accidents and serious injuries worldwide. For this reason, the death of more than one million
people worldwide from road traffic accidents was observed, and most of them were children
and adolescents [1]. The main types of collisions are driving under the influence of alcohol, not
paying attention to traffic lights, not noticing a car coming from behind when turning, and
environmental influences beyond their control [2]. Research on collision analysis is mainly
focused on road infrastructure. However, the results of the research show that the type of car,
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its size, the time of the accident, the season, and weather data also play an important role in the
occurrence of collisions. In addition, the incidence of collisions was higher in children, while the
number of injuries was higher in older men and women.
Methodology.
The CRIS database was used to collect data on road traffic incidents, and the records of
2003 and 2004 were reviewed. This analysis included data on injuries, accidents, collisions and
deaths available to accident investigators [3]. CRIS provides the terrain (location), time,
environmental messages, and driver and vehicle characteristics of the incident. To determine
the severity of an intersection accident, a bivariate analysis was performed on several crash
and driver-related variables, and ODDS coefficients were calculated for all classes within the
variable. Also, in the location map of traffic incidents at the intersection, the quartic kernel
function was used as a continuous surface to better analyze the crash density along the study
area using the kernel density method corresponding to the crash data:
𝑲
𝟐
(𝒙) = {𝟑𝝅
−𝟏
(𝟏 − 𝒙
𝑻
𝒙)
𝟐
𝒊𝒇𝒙
𝑻
𝒙 < 𝟏
𝟎 𝒐𝒕𝒉𝒆𝒓𝒘𝒊𝒔𝒆
Here 𝐾2(𝑥)𝐾2(𝑥)= 2-dimensional kernel function 𝑥𝑥 𝐾𝐾 is a radially symmetric unimodal
probability density function. Also, the deceleration rate is used to prevent accidents at the
intersection, and this value is calculated as follows:
𝑫𝑹𝑨𝑪
𝒇
=
(𝑽
𝒇
−𝑽
𝒔
)
𝟐
𝟐𝒅
= [
𝒎
𝒔
𝟐
]
The predicted density field at the intersection is determined by the following formula.
Density=
𝟏
𝒓𝒂𝒅𝒊𝒖𝒔
∑
[
𝟑
𝝅
𝒑
𝒐
𝒑
𝒕
(𝟏 − (
𝒅𝒊𝒔𝒕𝒂𝒏𝒄𝒆
𝒓𝒂𝒅𝒊𝒖𝒔
)
𝟐
)
𝟐
]
𝒏
𝒕=𝟏
For
𝐝𝐢𝐬𝐭𝐚𝐧𝐜𝐞
𝒕
< 𝒓𝒂𝒅𝒊𝒖𝒔
Here ii=1,…..,n1,….1 entry points or collapse;
𝑝
𝑜
𝑝
𝑡
=point density field value; The distance
of area
ii
to point
i
is calculated. Hot spot analysis was used to identify statistically significant
spatial clusters of low and high values. For each input feature, the method generated an output
feature with a z-score, a p-value, and a confidence level bin. The properties of the main null
hypothesis are values related to spatial randomness. Rejection of the null hypothesis is based
on the z-score and p-value, which indicates a statistically significant clustering or spread of the
characteristics or values associated with the trait. A very small p-value and a very high or low
z-score indicate a low probability that the cluster is the product of a random distribution [4].
The confidence levels used in the analysis were 90, 95, and 99 percent. This research aims to
help explore high-risk intersections through a safer road system. The heat location is calculated
for fatal kiddie collisions by the following equation.
𝑺
𝒊
= 𝟓𝑿
𝟏
+ 𝟑𝑿
𝟐
+ 𝟏. 𝟖𝑿
𝟑
+ 𝟏. 𝟑𝑿
𝟒
+ 𝑿
𝟓
Here
𝑋
1
- the number of fatal accidents,
𝑋
2
- the number of serious injuries,
𝑋
3
- the number
of relatively minor injuries,
𝑋
4
- the number of existing injuries, and
𝑋
5
- the number of healthy
participants without physical injuries.
Analysis and Results.
Studies have shown that traffic accidents and serious collisions at all intersections over a
5 years averaged more than 2 percent (2.2 %). Also, drivers’ inattention to vehicles coming from
behind when turning left and right was considered the main source of accidents, and in 23
percent of these cases, the participants of the road traffic accident suffered medical injuries. As
a result of the study of the intersections of San Antonio in the United States of America, it
became clear that since 2015, the number of fatal accidents in this state has decreased, and the
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number of serious and minor injuries has decreased. For example, 1447 serious injuries were
observed from 155 traffic accidents, which indicates that the number of injuries is 36.2 percent.
Through this table (Table 1), we analyze the relationship between the environment and the
road through the Chi-square test [5].
Number of
collisions
Changeable
df
P value
𝒙
𝟐
statistics
Minor
injuries
Serious
injuries
Range
Pedestrian
shooting
range
0
12
6.0 – 26.3
Give Way
3
18
1.0
Weather
information
4
0.076
30.8
Clean
126
1061
Cloudy
20
288
0.9 – 1.1
It’s raining
9
92
0.7 – 1.0
Other
0
3
0.1 – 1.1
Unknown
0
3
0.1 – 1.1
Check the
road
6
7.1
×
1
𝟎
−𝟔
33.9
A curved line
7
24
1.0
That's right
123
118
0.6 – 1.2
Lighting
condition
2
2.0
×
1
𝟎
−𝟕
30.8
Daytime
64
967
1.0
Night
86
456
1.2 – 1.5
Traffic control
6
3.4
×
1
𝟎
−𝟏𝟐
65.5
Table 1. Chi-square results (environmental and road-related factors)
Number of
collisions
Changeable
df
P value
𝒙
𝟐
statistics
Minor
injuries
Serious
injuries
Range
Young
3
2.2
×
1
𝟎
−𝟏𝟔
201.3
Around 18
years old
5
156
0.9 – 1.2
19-64
256
2350
1.0
65 or older
46
292
1.2 – 1.5
Unknown
8
139
0.3 – 0.4
Sex
2
2.2
×
1
𝟎
−𝟏𝟔
186.0
Male
224
1653
1.1 – 1.3
A woman
86
1207
1.0
96
Unknown
5
77
0.2 – 0.4
Table 2. Chi-Square Results (Driver-Related Factors)
The above tables show that the most number of collisions were observed among road
users related to the environment and the number of serious injuries was 1447. It was reported
that this is almost 90% more than the number of minor injuries. In addition, 64 light and 967
serious injuries were observed during the day, and light injuries make up 6% of the total
number of injuries. At night, a total of 552 collisions were observed, resulting in 87 light and
456 serious injuries, and light injuries accounted for 16% of the total number of injuries. The
situations related to the driver are as follows: a total of 1,877 injuries were observed among
men and 1,293 injuries among women.
Number of
collisions
Changeable
df
P value
𝒙
𝟐
statistics
Minor
injuries
Serious
injuries
Range
Day of the
week
1
0,001
10,5
99
1043
The season
3
0,003
13,9
Autumn
39
413
Spring
37
391
0.8 – 1.1
summer
46
351
0.8 – 1.1
Winter
33
292
0.7 – 0.9
Time of day
7
2.7
×
1
𝟎
−𝟏𝟎
48.7
12:00 to 3:00
30
101
3:00 to 6:00
8
45
0.6 – 1.2
6:00 to 9:00
18
186
0.6 – 1.0
9:00 a.m. to
12:00 p.m
13
199
0.6 – 0.9
from 12:00 to
15:00
17
277
0.6 – 0.9
15:00 to 18:00
22
268
0.4 – 0.7
18:00 to 21:00
25
216
0.5 – 0.8
21:00 to 23:00
22
155
0.7 – 1.1
Table 3. Chi-Square Results (Time of Collision)
The time ratio of collisions is as follows, the total number of injuries was 1142, 99 were
light and 1043 were serious. In addition, the number of injuries reached its peak in the autumn
season, and the lowest number of injuries was 325 in the winter season, while 294 injuries were
observed after midnight, i.e. from 12:00 to 15:00. The least injuries were observed from 3:00
to 6:00 (53 injuries). The three main causes of traffic accidents are human factors, vehicle
factors, and road environmental factors. Classical safety performance analysis addresses the
first two factors by showing the dangerous situation when vehicles approach each other in
situations where human error or vehicle failure could lead to an accident. Between 15:00–18:00
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and 18:00–21:00, the risk of a crash at the intersection was relatively low, possibly due to
congestion and slower traffic. Rainy weather reduced the odds of severe injury at intersections
(OR 0.73) [6]. Serious injuries are related to vehicle speed, and rainy weather and other adverse
weather conditions can force drivers to approach intersections with caution, which can result
in less serious injuries. Limited pedestrian and bicyclist traffic and more protective clothing in
cold weather may contribute to a relatively low chance of serious injury at winter intersections.
Conclusion.
This study used the CRIS database to determine how serious or minor injuries occurred
at intersections. In addition, the dependence of collisions on environment, time, and driver was
analyzed using the Chi-square program. The results of the study show that between 12:00 and
15:00, the number of accidents rises to its highest level, and represents its lowest level between
3:00 and 6:00 of the day. Traffic control, driver age v, gender, and lighting conditions are strong
predictors of serious crashes at intersections.
References:
Используемая литература:
Foydalanilgan adabiyotlar:
1.
Awadalla D. M. M., de Albuquerque F. D. B. Identification of risk factors associated with
fatal intersection crashes and assessment of the in-service safety performance of signalized
intersections and roundabouts in Abu Dhabi //Safety. – 2021. – Т. 7. – №. 4. – С. 69.
2.
Sharafeldin M., Farid A., Ksaibati K. Injury severity analysis of rear-end crashes at
signalized intersections //Sustainability. – 2022. – Т. 14. – №. 21. – С. 13858.
3.
Bernhardt M., Kockelman K. An analysis of pedestrian crash trends and contributing
factors in Texas //Journal of Transport & Health. – 2021. – Т. 22. – С. 101090.
4.
Kocatepe A. et al. Who might be affected by crashes? Identifying areas susceptible to crash
injury risk and their major contributing factors //Transportmetrica A: transport science. –
2019. – Т. 15. – №. 2. – С. 1278-1305.
5.
Goniewicz K. et al. Road accident rates: strategies and programmes for improving road
traffic safety //European journal of trauma and emergency surgery. – 2016. – Т. 42. – С. 433-
438.
6.
Fan F. Study on the cause of car accidents at intersections //Open Access Library Journal.
– 2018. – Т. 5. – №. 5. – С. 1-11.
