Prediction of Road Accident and Severity of Bangladesh Applying Machine Learning Techniques

Paul, Joy; Jahan, Zerin; Lateef, Kazi Fahim; Islam, Md. Robiul; Bakchy, Sagor Chandro

doi:10.1109/r10-htc49770.2020.9356987

Cited by 9 publications

(2 citation statements)

References 7 publications

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“…By conducting a comprehensive literature review, we were able to identify numerous knowledge gaps in the field of accident severity prediction. Most prior research studies only predicted the accident associated with one or two factors, which is insufficient for a real-world situation [11]; ii) many studies do not address the class imbalance problem; iii) unobserved heterogeneity; and iv) most studies only rely on a single accuracy measure to evaluate the performance of the algorithm. Therefore, the overarching goal of this study is to close the aforementioned knowledge gaps.…”

Section: Related Workmentioning

confidence: 99%

Road Accident Severity Prediction Using Machine Learning Algorithms

Kumar,

Teja Santosh

2022

IJCERT

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The majority of fatalities and serious injuries occur as a result of incidents involving motor vehicles. If the traffic management system is going to do its job of reducing the frequency and severity of traffic accidents, it needs a model for doing so. In this paper, we combine the results of three machine learning algorithms-logistic regression, decision tree, and random forest classifier-to build a predictive model. In order to forecast the severity of accidents in different regions, we used ML algorithms on a dataset of accidents from the United States. In addition, we examine vast quantities of traffic data, extracting helpful accident patterns in order to pinpoint the factors that have a direct bearing on road accidents and make actionable suggestions for improvement. When compared to two other ML algorithms, random forest performed best on accuracy. The severity rating in this paper is not meant to reflect the severity of injuries sustained, but rather how the accident affects traffic flow. Accident severity, decision trees, random forests, and logistic regression are all terms that are often used to describe this area of study.

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Section: Related Workmentioning

confidence: 99%

Road Accident Severity Prediction Using Machine Learning Algorithms

Kumar,

Teja Santosh

2022

IJCERT

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“…Venkat predicted the percentage of road accidents and determining factors of accidents using the random forest model, logistic regression model, decision tree model and k-nearest neighbor algorithms [5]. Paul et al performed simulations on the prediction of road accidents and their severity using the decision tree, random forest, multilayer perceptron, and naive Bayes models [6]. Schlogl examined the causes for road accidents using the random forest model and the XGBoost model.…”

Section: Introductionmentioning

confidence: 99%

Risk Prediction for Winter Road Accidents on Expressways

2021

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Road accidents caused by weather conditions in winter lead to higher mortality rates than in other seasons. The main causes of road accidents include human carelessness, vehicle defects, road conditions, and weather factors. If the risk of road accidents with changes in road weather conditions can be quantitatively evaluated, it will contribute to reducing the road accident fatalities. The road accident data used in this study were obtained for the period 2017 to 2019. Spatial interpolation estimated the weather information; geographic information system (GIS) and Shuttle Radar Topography Mission (SRTM) data identified road geometry and accident area altitude; synthetic minority oversampling technique (SMOTE) addressed the data imbalance problem between road accidents due to weather conditions and from other causes, and finally, machine learning was performed on the data using various models such as random forest, XGBoost, neural network, and logistic regression. The training- to test data ratio was 7:3. Random forest model exhibited the best classification performance for road accident status according to weather risks. Thus, by applying weather data and road geometry to machine learning models, the risk of road accidents due to weather conditions in the winter season can be predicted and provided as a service.

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An Improved Machine Learning Algorithm for Crash Severity and Fatality Insight in VANET Network

Bharathi

Durgadevi

2023

Engineering Cyber-Physical Systems and Critical Infrastructures

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Prediction of Road Accident and Severity of Bangladesh Applying Machine Learning Techniques

Cited by 9 publications

References 7 publications

Road Accident Severity Prediction Using Machine Learning Algorithms

Road Accident Severity Prediction Using Machine Learning Algorithms

Risk Prediction for Winter Road Accidents on Expressways

An Improved Machine Learning Algorithm for Crash Severity and Fatality Insight in VANET Network

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