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.

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 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.

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 Identifying Road Traffic Accidents Hotspots Areas Using GIS in Ethiopia: A Case Study of Hosanna Town

2019 ◽  
Vol 20 (2) ◽  
pp. 123-132
Author(s):  
Temesgen Haile Hayidso ◽  
Dessalegn Obsi Gemeda ◽  
Ashenif Melese Abraham
Keyword(s):  
Global Position System ◽  
Road Traffic Accident ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Hot Spot ◽  
Human Life ◽  
Position System ◽  
Road Traffic Accidents ◽  
Accident Data

Abstract Due to increasing human population and the number of vehicles, road traffic accident has significant influence on human life and economic development. In the present study, road traffic accident data of three years (2015-2017) were obtained from Hosanna Town Traffic Police Department, and Hosanna Town Transport Authority in Ethiopia. The Global Position System was used to know X, Y coordinates of the accident locations. Global Position System point data and accident data were added to road network data using the ‘Joins and relates’ function in ArcGIS. The results of the study showed a total of 241 Road traffic accidents (RTAs) were occurred in the town from which about 208 victims occurred on people and 33 damaged properties. Based on severity and frequencies of RTAs top nine hot spot areas were identified which requires high attention to protect people and property from damage and loss. Thus, the government and other concerned stakeholders should provide public education and awareness creation to reduce risk of fatalities and property damage due to RTAs in Hosanna town.

scholarly journals Community Causes of Death in the Central Region of Ghana, the Missing Piece in Mortality Data

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Patrick Kafui Akakpo ◽  
Emmanuel Gustav Imbeah ◽  
Francis Agyarko-Wiredu ◽  
Kennedy Awlavi ◽  
Kwame Baah-Amoh ◽  
...  
Keyword(s):  
Cause Of Death ◽  
Central Region ◽  
Traffic Accidents ◽  
Causes Of Death ◽  
Road Traffic ◽  
Noncommunicable Diseases ◽  
Mortality Data ◽  
Cardiovascular Disorders ◽  
Road Traffic Accidents ◽  
Causes Of Deaths

Objective. Mortality data from hospitals in Ghana suggest a changing mortality trend with noncommunicable diseases (cardiovascular disorders) replacing communicable diseases as the leading cause of death. Our objective was to find out the causes of deaths in the communities of the Central Region of Ghana and raise awareness of these causes of deaths while highlighting the differences that exist between data obtained from the community and that obtained from the hospital. Method. Mortality data from Coroner’s autopsies mostly provide data about the causes of deaths in the community (out of hospital). A retrospective descriptive study of Coroner’s autopsy data at the Cape Coast Teaching Hospital was carried out over a six-year period. The various causes of death were categorized according to broad headings (accidents/injuries/poisoning, cardiovascular, infections, metabolic, neoplasms, and others). Results. A total of 1187 autopsies were reviewed of which 990 (83.4%) were Coroner’s cases. Of these Coroner’s cases, 719 (72.6%) were male and 271 (27.4%) were female. 521 (52.6%) of victims were young adults (18–44 years), and majority of deaths were unnatural (due to accidents, injuries, and poisoning) (64.1%), followed by the general category of others (15.3%). Cardiovascular deaths (6.6%) were fourth after infections (9.8%). In the leading category, most deaths were due to road traffic accidents (50.4%) as occupants of vehicles and motorcycles (28.7%) and as pedestrians (21.7%). Deaths due to road traffic accidents were followed by deaths due to drowning (14.96%). Conclusion. Although noncommunicable diseases are still the leading causes of death outside the hospital, most of the deaths are due to road traffic accidents and drowning. This is at variance with hospital data that suggest that the leading noncommunicable diseases are cardiovascular disorders and cancer. Again, like data derived from hospitals, infections remain a major cause of death in the Central Region of Ghana. Studies combining the causes of death derived from Coroner’s autopsies and communities and from medical certificates of cause of death will present a better picture of the leading causes of death in the Central Region and reveal the true nature of noncommunicable diseases that currently form our largest disease burden.

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.

ADHD is a Risk Factor of Road Crashes: A Purposive Study of Finding Relation between ADHD and Road Traffic Accident in Bangladesh

2018 ◽  
Vol 9 (08) ◽  
pp. 20531-20536
Author(s):  
Nusrat Shamima Nur ◽  
M. S. l. Mullick ◽  
Ahmed Hossain
Keyword(s):  
Risk Factor ◽  
Road Traffic Accident ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Fatality Rate ◽  
Road Traffic Accidents ◽  
Road Crashes ◽  
Road Crash ◽  
Day By Day

Background: In Bangladesh fatality rate due to road traffic accidents is rising sharply day by day. At least 2297 people were killed and 5480 were injured in road traffic accidents within 1st six months of 2017.Whereas in the previous year at 2016 at least 1941 people were killed and 4794 were injured within the 1st six months. No survey has been reported in Bangladesh yet correlating ADHD as a reason of impulsive driving which ends up in a road crash.

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