Systemic approach to auditing road traffic accident black spots

Kurakina, Elena; Kravchenko, Pavel; Brylev, Ilya; Rajczyk, Marlena

doi:10.1016/j.trpro.2020.10.039

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…Since the goal of the national road maintenance and development program of the Republic of Lithuania is zero fatalities in road transport by 2050 [36], we need to create a research model examining extremely dangerous sections, which are marked as "black spots". The road section has many parameters for the origin of the traffic accident, but it is closely related to the participants in the traffic accident, so it is difficult to assess all the causes of the occurrence of the accident, but the influence of the human factor can be reduced.…”

Section: Methodsmentioning

confidence: 99%

“…At higher driving speeds, one of the human factors, such as reaction time, should not decrease, but the vehicle travels a greater distance in the same time. It is an interesting fact that the driver's perception of the environment differs at different driving speeds [36]. Sight distance defined as the length of the roadway that the driver can see [37].…”

Section: Introductionmentioning

confidence: 99%

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Establishing Emergency Sections on Land Roads in Order to Improve the Quality of Transport Services, Creating Comfortable Conditions for International and Local Traffic

Zaranka¹,

Matijošius²,

Radvilaite³

et al. 2023

Adv. Sci. Technol. Res. J.

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The rapid increase in the number of cars in Lithuania and abroad every year (Statistics of traffi c accidents in Lithuania), ensuring road safety is becoming an increasingly important and relevant aspect for road users and other responsible institutions. In the statistics of traffi c accidents in Lithuania, no information is provided according to the nature of the vehicle collision, nor is there any information on the damage caused by the vehicle to the victim according to the type of collision. The work analyzes the security situation and emergency factors based on statistics. Factors determining the occurrence of traffi c accidents are analyzed. A visual analysis of "black spots" was performed to determine the main factors of higher accidents. Statistical data and comparative analysis methods are used for the research. Calculated traffi c incidents and the amount of damage caused by them are analyzed according to the type of collision.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Establishing Emergency Sections on Land Roads in Order to Improve the Quality of Transport Services, Creating Comfortable Conditions for International and Local Traffic

Zaranka¹,

Matijošius²,

Radvilaite³

et al. 2023

Adv. Sci. Technol. Res. J.

View full text Add to dashboard Cite

show abstract

“…Two separate research studies, conducted by Sun et al (2021) and Kurakina et al (2020), focused on analyzing the rates of injuries and fatalities resulting from traffic collisions based on the primary function of different types of roads. The classification of road types included administrative roads, functional roads, urban expressways, and urban general roads.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Factors Influencing Traffic Accidents: An Analysis of Black Spots and Decision Tree for Injury Severity

Abdullah,

Sipos

2023

Period. Polytech. Transp. Eng.

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This research aimed to examine the spatial distribution of road traffic accidents in Budapest, Hungary. The primary objective was to identify the factors associated with traffic accidents on the city's transportation network and to determine the locations of the most frequent accidents during peak and off-peak hours. A quantitative methodology was employed in this study, utilizing a dataset of recent accidents that occurred between 2019 and 2021, classified into peak and off-peak incidents. The data was analyzed using Python software and Quantum Geographic Information System (QGIS) tools for big data analytics. These programs enabled the creation of spatial maps of the study area and the identification of accident spots based on latitude and longitude information. A decision tree classification approach was used in the machine-learning method implemented with Python software. Additionally, the dataset file was uploaded to QGIS, which applied the heatmap (Kernel Density Estimation) algorithm to identify accident hotspots. The study findings revealed that the city center was the most common location for accidents overall, with peak and off-peak times, lanes, and days of the week investigated as potential contributing factors. The target variable was the number of accidents involving serious and minor injuries, which were found to be significantly associated with the identified accidents in this study.

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“…In Turkey, a study utilizes GIS and statistical methods to identify traffic accident hotspots on state roads, pinpointing black spots and reducing accidents [12] . Additionally, studies focusing on different road types have revealed varying rates of injury and death resulting from traffic collisions, with administrative roads having the highest death rate, followed by functional roads, while urban general roads exhibit higher accident rates compared to urban expressways [13,14] .…”

Section: Introductionmentioning

confidence: 99%

Exploring traffic accidents patterns: Spatial distribution and socio-economic determinants

Abdullah,

Sipos

2023

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<p class="Els-keywords">This study employs a machine learning methodology, specifically the decision tree algorithm, in conjunction with the Quantum Geographic Information System (QGIS), to conduct a rigorous analysis of traffic accident data. The research aims to investigate various factors associated with traffic accidents, with a particular emphasis on their spatial distribution and the socio-economic determinants contributing to recurring accidents caused by drivers. The study focuses on the city of Duhok, located in the Kurdistan Region of Iraq, and utilizes a questionnaire to collect data from drivers regarding accident locations and the frequency of accidents within the past decade (from 2010 to 2020). The findings of the study reveal that the city center experiences the highest concentration of accidents, while severe collisions tend to occur in specific “black spots” scattered across the city’s road network. The decision tree model, employed to classify drivers with multiple accidents, identifies the primary causes of accidents as traffic conditions, traffic law violations, and overspeeding. Furthermore, the accident locations are found to be influenced by various factors, including different types of road hierarchy. The age and gender of drivers also contribute to accident patterns. These research findings have practical implications for enhancing road safety measures and reducing the frequency of traffic accidents. The utilization of machine learning techniques, combined with the analysis of spatial data through QGIS, provides a comprehensive understanding of the underlying factors contributing to accidents. Moreover, this research contributes novel insights to the field of road traffic accidents and safety, particularly in the context of the city of Duhok in Kurdistan Region, Iraq, and provides a valuable reference for future studies in the domain of road safety and urban planning.</p>

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Systemic approach to auditing road traffic accident black spots

Cited by 7 publications

References 14 publications

Establishing Emergency Sections on Land Roads in Order to Improve the Quality of Transport Services, Creating Comfortable Conditions for International and Local Traffic

Establishing Emergency Sections on Land Roads in Order to Improve the Quality of Transport Services, Creating Comfortable Conditions for International and Local Traffic

Exploring the Factors Influencing Traffic Accidents: An Analysis of Black Spots and Decision Tree for Injury Severity

Exploring traffic accidents patterns: Spatial distribution and socio-economic determinants

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