scholarly journals Spatial and Temporal Distribution Analysis of Traffic Accidents Using GIS-Based Data in Harbin

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Meina Wang ◽  
Jing Yi ◽  
Xirui Chen ◽  
Wenhui Zhang ◽  
Tiangang Qiang
Keyword(s):  
Traffic Safety ◽  
Road Network ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Network Density ◽  
Road Traffic Accidents ◽  
Urban Centers ◽  
Accident Severity ◽  
Accident Data ◽  
Density Analysis

Road traffic safety is a social issue of widespread concern. It is important for traffic managers to understand the distribution patterns of road traffic accidents. To this end, this study examines the spatial and temporal patterns of road traffic accidents from both accident frequency and accident severity perspectives. Road traffic accident data from 2016 to 2018 in Harbin, China, were used for the analysis. First, the spatial localization of accidents was completed using geocoding, and the localized accident data were classified by season. Then, density analysis was performed both with and without considering road network density. The results of the density analysis showed that when road network density was considered, accidents were mainly distributed in urban centers, while accidents were more dispersed when road network density was not considered. Third, a cluster analysis considering accident severity found that low-severity accident clusters occurred mostly in urban centers. High-severity accident clusters were mostly present in suburban areas. Finally, the results of these two methods are shown by using the comap technique. Areas of the city with a high frequency and severity of crashes in each season were identified. This study will help traffic management to have a more visual and intuitive understanding of the urban traffic safety situation and to take targeted measures to improve it accordingly.

scholarly journals Business Intelligence and Data Warehouse Technologies for Traffic Accident Data Analysis in Botswana

2021 ◽  
Author(s):  
Monkgogi Mudongo ◽  
Edwin Thuma ◽  
Nkwebi Peace Motlogelwa ◽  
Tebo Leburu-Dingalo ◽  
Pulafela Majoo
Keyword(s):  
Data Warehouse ◽  
Business Intelligence ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Road Traffic Accidents ◽  
Managerial Decision ◽  
Vehicle Registration ◽  
Accident Severity ◽  
Accident Data ◽  
Causes Of Deaths

Road traffic accidents are a serious problem for the nation of Botswana. A large amount of money is used to compensate those who are affected by road accidents. Traffic accidents are one of the major causes of Deaths in Botswana. It is important for relevant organizations to have a reliable source of data for accurate evaluation of traffic accidents. Similarly, data on vehicle registration must be transformed and be readily available to assist managerial decision makers. In this article, we deploy a Business Intelligence (BI) and Data Warehouse (DW) solution in an attempt to assist the relevant departments in their road traffic accidents and vehicle registration evaluation. In Our evaluation of the traffic accidents our findings suggest that across accident severity, Damage Only accidents had the most interesting recent trend with a 11.93% decrease in the last 3 years on record. Count of Accident Severity for Damage Only accidents dropped from 13,491 to 11,881 between 2018 and 2020 whilst Minor accidents experienced the longest period of growth. Most accidents take place in rural locations and more accidents take place during the weekend. At 28,439, Sunday had the highest number of accidents and was 47.59% higher than Wednesday, which had the lowest count of accidents at 19,269. The results for vehicle registration reveal that the number of vehicle registration decreased for the last 3 years on record. The number of vehicles registered dropped from 65535 to 24457 during its steepest decline between 2019 and 2021.

ASSESSMENT OF THE PRECISION OF DATA COLLECTED ABOUT THE TRAFFIC ACCIDENTS WITH PROPERTY DAMAGE ONLY IN CLAIM HANDLING PROCESS BY INSURANCE COMPANIES

Transport ◽  
2014 ◽  
Vol 32 (2) ◽  
pp. 160-166
Author(s):  
Erik Ernits ◽  
Dago Antov ◽  
Anton Kott
Keyword(s):  
Traffic Safety ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Insurance Companies ◽  
Road Traffic Accidents ◽  
Quality Of Data ◽  
Property Damage ◽  
Traffic Conflicts ◽  
Accident Data ◽  
Survey Results

The number of serious road traffic accidents is decreasing in all European countries. Based on the trends and directions in the past it may be predicted that in longer perspective the number of serious road traffic accidents will decrease remarkably. This will create a situation where it is more and more difficult to ensure the reliability of traffic safety analyses performed by statistical methods. There are two possibilities to decrease the problem: either to carry out in-depth investigations of serious road traffic accidents and/or investigate also Property Damage Only (PDO) traffic accidents and traffic conflicts in addition to serious traffic accidents. The key issue in using the PDO accident data is its precision. The present paper is attempting to enlighten the area, and assess the quality of data of PDO road traffic accidents collected by insurance providers by example of Estonia. The survey results show that in spite of certain shortcomings, the PDO road traffic accident data collected by insurance provider is valuable to be used in traffic safety analyses.

scholarly journals Assessing inequality, irregularity, and severity regarding road traffic safety during COVID-19

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lei Lin ◽  
Feng Shi ◽  
Weizi Li
Keyword(s):  
New York ◽  
Los Angeles ◽  
Traffic Safety ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Human Mobility ◽  
Critical Conditions ◽  
Road Traffic Safety ◽  
Accident Data ◽  
The Impact

AbstractCOVID-19 has affected every sector of our society, among which human mobility is taking a dramatic change due to quarantine and social distancing. We investigate the impact of the pandemic and subsequent mobility changes on road traffic safety. Using traffic accident data from the city of Los Angeles and New York City, we find that the impact is not merely a blunt reduction in traffic and accidents; rather, (1) the proportion of accidents unexpectedly increases for “Hispanic” and “Male” groups; (2) the “hot spots” of accidents have shifted in both time and space and are likely moved from higher-income areas (e.g., Hollywood and Lower Manhattan) to lower-income areas (e.g., southern LA and southern Brooklyn); (3) the severity level of accidents decreases with the number of accidents regardless of transportation modes. Understanding those variations of traffic accidents not only sheds a light on the heterogeneous impact of COVID-19 across demographic and geographic factors, but also helps policymakers and planners design more effective safety policies and interventions during critical conditions such as the pandemic.

scholarly journals Comparative Study of Machine Learning Classifiers for Modelling Road Traffic Accidents

2022 ◽  
Vol 12 (2) ◽  
pp. 828
Author(s):  
Tebogo Bokaba ◽  
Wesley Doorsamy ◽  
Babu Sena Paul
Keyword(s):  
Machine Learning ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Real Life ◽  
Support Vector ◽  
Road Traffic Accidents ◽  
Machine Learning Classifiers ◽  
Reduction Techniques ◽  
Learning Classifiers ◽  
Accident Data

Road traffic accidents (RTAs) are a major cause of injuries and fatalities worldwide. In recent years, there has been a growing global interest in analysing RTAs, specifically concerned with analysing and modelling accident data to better understand and assess the causes and effects of accidents. This study analysed the performance of widely used machine learning classifiers using a real-life RTA dataset from Gauteng, South Africa. The study aimed to assess prediction model designs for RTAs to assist transport authorities and policymakers. It considered classifiers such as naïve Bayes, logistic regression, k-nearest neighbour, AdaBoost, support vector machine, random forest, and five missing data methods. These classifiers were evaluated using five evaluation metrics: accuracy, root-mean-square error, precision, recall, and receiver operating characteristic curves. Furthermore, the assessment involved parameter adjustment and incorporated dimensionality reduction techniques. The empirical results and analyses show that the RF classifier, combined with multiple imputations by chained equations, yielded the best performance when compared with the other combinations.

scholarly journals A new approach to solving the problem of road traffic injuries in agricultural areas

2001 ◽  
Vol 82 (5) ◽  
pp. 369-397
Author(s):  
I. G. Faizullin
Keyword(s):  
Traffic Accidents ◽  
Road Traffic ◽  
Severity Index ◽  
Road Traffic Accidents ◽  
New Approach ◽  
Accident Severity ◽  
Depth Study ◽  
The Russian Federation ◽  
The Republic ◽  
Agricultural Areas

In the Republic of Tatarstan from 1995 to the first half of 1999, there were 18,376 road traffic accidents (RTAs). They affected 23558 people, killed 3525 and injured 20033. The accident severity index averaged 14.8 during that period. Downward trends in the severity of accidents in 1995, 1996, 1997, 1998 were rather telling: 26.9; 17.6; 16.9; 13.7; 11.8. In spite of that, Tatarstan looks unfavorable against the background of other territories of the Russian Federation. In order to identify the cause-and-effect relations of the severity of traffic accidents, we conducted an in-depth study of them in the territory of cities and agricultural districts during this period.

ABOUT ADDITIONAL INDICATORS ROAD TRAFFIC ACCIDENTS IN THE RUSSIAN FEDERATION

2021 ◽  
pp. 29-34
Author(s):  
О.Н. Кузьмин ◽  
Е.В. Дедюлин
Keyword(s):  
Russian Federation ◽  
Road Safety ◽  
Road Network ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Road Traffic Accidents ◽  
The Russian Federation ◽  
Network Safety

В статье анализируется информативность основных показателей аварийности, возможные неверные представления о повышении безопасности дорожного движения при снижении количества ДТП, необходимость учета степени безопасности дорожной сети при выборе мер, направленных на повышение безопасности дорожного движения, а также комплесная оценка этих мер и степени безопасности дорожной сети. The article analyzes the informativeness of the main indicators of accidents, possible misconceptions about improving road safety while reducing the number of accidents, as well as the need to take into account the degree of road safety when choosing measures aimed at improving road safety, assessing such measures in combination with the degree of road network safety.

scholarly journals Road traffic accidents on Senai-Desaru expressway

2018 ◽  
Vol 250 ◽  
pp. 02002 ◽  
Author(s):  
Nordiana Mashros ◽  
SittiAsmah Hassan ◽  
Yaacob Haryati ◽  
Mohd Shahrir Amin Ahmad ◽  
Ismail Samat ◽  
...  
Keyword(s):  
Traffic Safety ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Contributing Factors ◽  
Property Damage ◽  
Crash Frequency ◽  
Crash Data ◽  
Run Off ◽  
Accident Data ◽  
Road Segments

Understanding and prioritising crash contributing factors is important for improving traffic safety on the expressway. This paper aims to identify the possible contributory factors that were based on findings obtained from crash data at Senai-Desaru Expressway (SDE), which is the main connector between the western and eastern parts of Johor, Malaysia. Using reported accident data, the mishaps that had occurred along the 77.2 km road were used to identify crash patterns and their possible related segment conditions. The Average Crash Frequency and Equivalent Property Damage Only Average Crash Frequency Methods had been used to identify and rank accident-prone road segments as well as to propose for appropriate simple and inexpensive countermeasures. The results show that the dominant crash type along the road stretches of SDE had consisted of run-off-road collision and property damage only crashes. All types of accidents were more likely to occur during daytime. Out of the 154 segments, the 4 most accident-prone road segments had been determined and analysed. The results obtained from the analyses suggest that accident types are necessary for identifying the possible causes of accidents and the appropriate strategies for countermeasures. Therefore, this accident analysis could be helpful to relevant authorities in reducing the number of road accidents and the level of accident severity along the SDE.

scholarly journals Empirical Modelling for exploring the factors contributing to disability severity from road traffic accidents in Thailand

1970 ◽  
Vol 6 (2) ◽  
pp. 176-185
Author(s):  
Jaratsri Rungrattanaubol ◽  
Anamai Na-udom ◽  
Antony Harfield
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Traffic Accidents ◽  
Injury Severity ◽  
Road Traffic ◽  
Road Traffic Accidents ◽  
Empirical Modelling ◽  
Standard Data ◽  
Accident Data ◽  
Computer Based

This paper introduces a computer-based model for predicting the severity of injuries in road traffic accidents. Using accident data from surveys at hospitals in Thailand, standard data mining techniques were applied to train and test a multilayer perceptron neural network. The resulting neural network specification was loaded into an interactive environment called EDEN that enables further exploration of the computer-based model. Although the model can be used for the classification of accident data in terms of injury severity (in a similar way to other data mining tools), the EDEN tool enables deeper exploration of the underlying factors that might affect injury severity in road traffic accidents. The aim of this paper is to describe the development of the computer-based model and to demonstrate the potential of EDEN as an interactive tool for knowledge discovery.

scholarly journals Problems of road traffic injuries and road traffic safety in Russia

2020 ◽  
pp. 27-34
Author(s):  
S. S. Aleksanin ◽  
S. V. Shport
Keyword(s):  
Russian Federation ◽  
Traffic Safety ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Alcohol Intoxication ◽  
Road Traffic Accidents ◽  
The Public ◽  
The Road ◽  
Road Traffic Safety ◽  
The Russian Federation

Relevance. The paper is devoted to problems of ensuring road traffic safety in Russia, which is the public task of great importance involving the implementation of the policy for protecting people's health, life, and property.Intention. To look for ways of optimizing and implementing the measures aimed at preventing the technogenic emergencies.Methodology. To analyze the indicators of road traffic accidents in the Russian Federation over five years as well as the federal laws in the field of road traffic safety.Results and Discussion. Risk factors of traffic accidents include: alcohol intoxication, speed limit exceeded, overtaking in the wrong place, driver talking, smoking while driving, driver fatigue. In the Russian Federation, there is a persistent downward trend in the number of accidents. In 2019, 164,358 traffic accidents (-2.2 %; all comparisons vs 2018) occurred, 16,981 (-6.8 %) persons died, 210,877 (-1.9 %) persons were injured. According to the Ministry of Internal Affairs, the number of accidents due violation of the Road Rules by drivers was 146,688 (-1 %); 14,420 (-5.7 %) persons died and 195,037 (-0.8 %) persons were injured. In 2019, road traffic accidents caused by drunk drivers tended to decrease (12,040; -3.5 %); 11,510 (-4 %) persons died and 160,725 (-0.4 %) persons were injured.Conclusion. Drivers' health is directly related to road traffic safety; health promotion would contribute to decreasing the number of road traffic accidents and traffic-related injuries.

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