Road Accident Data Collection Systems in Developing and Developed Countries: A Review

Rabbani, Muhammad Babar Ali; Musarat, Muhammad Ali; Alaloul, Wesam Salah; Ayub, Saba; Bukhari, Hamna; Altaf, Muhammad

doi:10.30880/ijie.2022.14.01.031

Cited by 12 publications

(7 citation statements)

References 27 publications

(32 reference statements)

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“…There is a vast quantity of data on road accidents worldwide. Rabbani et al [13] reviewed and compared accident data collection systems in developing and developed countries. They noted that developing countries such as Pakistan frequently use manual data recording systems while the developed nations such as Germany and France use automated systems with the latest technology.…”

Section: B Data Sourcesmentioning

confidence: 99%

“…Data availability is a key challenge for geospatial analysis. In the developed countries it has been noted that there is generally data available for analysis collected by government organizations that manage road accidents, whereas developing nations may not have the same quantity, quality, and reliability of comparable data [13]. Further, in more recent work, there is a requirement to include street view imagery in accident analysis [8].…”

Section: G Limitations and Challenges Of Current Approachesmentioning

confidence: 99%

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Geospatial Factors Applied to Road Accidents: A Review

Watson,

Ryan

2024

JAIT

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Road accidents are a major source of trauma worldwide due to increasing numbers of vehicles and drivers. Considerable data has been collected on road accidents and this is frequently published as open data on the internet. These datasets include parameters such as accident type and number of fatalities as well as environmental variables such as road type, demographics, and area infrastructure. Geospatial analysis provides a means of understanding spatial factors that influence road accidents such as the built infrastructure, natural environment features such as hills and vegetation, traffic volume, and road design and construction. Geospatial visualization techniques can also help identify hotspots and blackspots. The Moran I, Getis-Ord, and Kernel Density Estimation techniques are the most commonly-used geospatial tests and their application is reported in many papers. This paper provides a review of geospatial factors that are relevant to road accidents.

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Section: B Data Sourcesmentioning

confidence: 99%

Section: G Limitations and Challenges Of Current Approachesmentioning

confidence: 99%

Geospatial Factors Applied to Road Accidents: A Review

Watson,

Ryan

2024

JAIT

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“…Practitioners from all over the world, who have access to police accident data containing accident descriptions and optional variables describing the type of participants and collision, as well as the cause of the accident, can start to build their region-specific 3AT prediction models. It should be emphasized that most developing countries use accident recording forms that contain at least text fields with accident descriptions [79].…”

Section: H For Practitionersmentioning

confidence: 99%

Predicting the Type of Road Traffic Accident for Test Scenario Generation

Bäumler,

Prokop

2024

IEEE Access

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Automated driving systems should be able to avoid road traffic accidents and drive more safely than human drivers do. Test scenarios derived from real-world data such as police accident data can help assess the safety performance of automated driving systems. In many countries, the police collect data and information about nearly every accident, resulting in a representative sample. However, the collected accident data often do not contain the exact conflicts that cause an accident. Therefore, we estimated the globally known three-digit accident type for German police accident data, describing accident-causing conflicts. The data supplemented by the estimated three-digit accident type can then be used in subsequent test scenario generation. Accordingly, this study presents the first classification model for predicting 30 types of turning accidents. We tested a CatBoost model and a large-language model called BERT, using different feature sets and model designs. Overall, the CatBoost model performed best when using accident descriptions and non-text features such as collision type. Anomaly detection performed before model training revealed additional knowledge-driven miscoding in the police data collection. In conclusion, the model can predict common accident types, such as left-turns with an oncoming straight-ahead driver. In contrast, the model fails to predict rare accident types, such as left-turns (with a lit arrow sign) with oncoming traffic. Future studies should focus on optimizing the developed model, handling data imbalances, and building models using police accident data from other countries.

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“…However, rather than seeing this as a limitation, it is an open window to explore other factors rather than human factors, which have been greatly explored [26][27][28][29][30][31]. Also, the use of a year's worth of data in this study is an attempt to take into account the fact that most developing and undeveloped nations are only getting started with accident databases [33,34].…”

Section: Traffic Crash Data: Uk 2020mentioning

confidence: 99%

Machine Learning for Road Traffic Accident Improvement and Environmental Resource Management in the Transportation Sector

Megnidio-Tchoukouegno

Adedeji

2023

Sustainability

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Despite the measures put in place in different countries, road traffic fatalities are still considered one of the leading causes of death worldwide. Thus, the reduction of traffic fatalities or accidents is one of the contributing factors to attaining sustainability goals. Different factors such as the geometric structure of the road, a non-signalized road network, the mechanical failure of vehicles, inexperienced drivers, a lack of communication skills, distraction and the visual or cognitive impairment of road users have led to this increase in traffic accidents. These factors can be categorized under four headings that are: human, road, vehicle factors and environmental road conditions. The advent of machine learning algorithms is of great importance in analysing the data, extracting hidden patterns, predicting the severity level of accidents and summarizing the information in a useful format. In this study, three machine learning algorithms for classification, such as Decision Tree, LightGBM and XGBoost, were used to model the accuracy of road traffic accidents in the UK for the year 2020 using their default and hyper-tuning parameters. The results show that the high performance of the Decision Tree algorithm with default parameters can predict traffic accident severity and provide reference to the critical variables that need to be monitored to reduce accidents on the roads. This study suggests that preventative strategies such as regular vehicle technical inspection, traffic policy strengthening and the redesign of vehicle protective equipment be implemented to reduce the severity of road accidents caused by vehicle characteristics.

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Road Accident Data Collection Systems in Developing and Developed Countries: A Review

Cited by 12 publications

References 27 publications

Geospatial Factors Applied to Road Accidents: A Review

Geospatial Factors Applied to Road Accidents: A Review

Predicting the Type of Road Traffic Accident for Test Scenario Generation

Machine Learning for Road Traffic Accident Improvement and Environmental Resource Management in the Transportation Sector

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