Risk Prediction for Winter Road Accidents on Expressways

Kim, Daeseong; Jung, Sangyun; Yoon, Sanghoo

doi:10.3390/app11209534

Cited by 11 publications

(10 citation statements)

References 31 publications

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“…The definition of the correlation coefficient between the prediction P and the truth T is shown in Formula (8).…”

Section: Experiments and Analysismentioning

confidence: 99%

“…Comparative experiments show that the proposed method has better performance in terms of sensitivity and accuracy. Combined with traffic accidents, weather information, the height difference between the accident site and the surroundings, and whether there are tunnels and bridges, Kim et al [8] use models such as random forest, XGBoost, neural networks, and logical regression to deeply mine the data and predict the risk of accidents caused by weather conditions in the winter. But the accuracy of various influencing factors remains to be improved.…”

Section: Introductionmentioning

confidence: 99%

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Improving Traffic Safety through Traffic Accident Risk Assessment

Zhou

Zhang³

2023

Sustainability

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The continuous development of sensors and the Internet of Things has produced a large amount of traffic data with location information. The improvement of traffic safety benefits from the availability of traffic accident data. Managers can patrol and control relevant areas in advance with limited police resources, according to the short-term traffic accident predictions. As a result, the possibility of accidents can be reduced, and the level of traffic safety can be improved. The traditional approach to accident prediction relies too much on the subjective experience of traffic managers. Inspired by the deep learning technology in the field of computer vision, this study first divides the road network into regular grids and takes the number of traffic accidents in each grid as the pixel value of an image. Then, a traffic accident prediction approach based on a bi-directional ConvLSTM U-Net with densely connected convolutions (BCDU-Net) is proposed. This method mines the regular information hidden in the accident data and introduces densely connected convolutions to further extract the deep spatial-temporal features contained in the traffic accident sequence. Thus, the issues of gradient disappearance and model over-fitting caused by the traditional model in model training can be avoided. Finally, the simulation experiment is carried out on the historical traffic accident data of Yinzhou District, Ningbo City. Results show that BCDU-Net has better accuracy and precision than other models in three data sets: motor vehicle accidents, non-motor vehicle accidents, and single-vehicle accidents. Therefore, the BCDU-Net is more suitable for traffic accident prediction and has good application prospects for improving road safety.

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“…The definition of the correlation coefficient between the prediction P and the truth T is shown in Formula (8).…”

Section: Experiments and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving Traffic Safety through Traffic Accident Risk Assessment

Zhou

Zhang³

2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…LR is well-known as a classification method with mapping results of the linear functions to the sigmoid functions [41,42]. Similarly to NB, implementation of the method is easy and it can effortlessly be extended to multi-class problems.…”

Section: Logistic Regressionmentioning

confidence: 99%

Comparative Study of Machine Learning Classifiers for Modelling Road Traffic Accidents

2022

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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.

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“…Recently, the authors in [17], [18], [28] explored the use of Machine Learning models to forecast road accidents occurrences. Specifically, they trained an ensemble of treebased models on multivariate, fine-grained weather datasets.…”

Section: Related Workmentioning

confidence: 99%