A Review of Road Traffic Accident Prediction Methods

Wang, Shunshun; Yan, Chao; Yong, Shao

doi:10.11648/j.ajmse.20230803.12

Cited by 2 publications

(3 citation statements)

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Section: Traffic Accident Factors To Explorementioning

confidence: 99%

“…Additionally, according to another recent study by Shunshun et al [34], urbanization has become one of the major causes of traffic accidents and urban safety. Hence, Shunshun et al's [34] study used the traffic accident prediction model from the research published in English journals and provided a detailed literature on the traffic accident prediction methodologies. In addition, Shunshun et al's [34] study further provides in-depth descriptions related to conventional statistical methods, neural networks, machine learning, data mining, and time series analysis.…”

Section: Traffic Accident Factors To Explorementioning

confidence: 99%

“…Hence, Shunshun et al's [34] study used the traffic accident prediction model from the research published in English journals and provided a detailed literature on the traffic accident prediction methodologies. In addition, Shunshun et al's [34] study further provides in-depth descriptions related to conventional statistical methods, neural networks, machine learning, data mining, and time series analysis. The methodologies are efficient for analyzing the road accident factors, prevention factors to ensure urban safety, building and enhancing prediction models.…”

Section: Traffic Accident Factors To Explorementioning

confidence: 99%

See 2 more Smart Citations

Using Multilayer Perceptron Neural Network to Assess the Critical Factors of Traffic Accidents

Ruangkanjanases,

Sivarak,

Weng

et al. 2024

HighTech. Innov. J.

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This study is based on the traffic accident data of Taoyuan City from the government's open data. The study compiled the data set of traffic accidents in Taiwan from 2012 to 2017, and six classifiers were applied to evaluate the effectiveness of traffic accident prediction with the number of injuries as the prediction target. In order to verify the classifier's stability, cross-validation was used to evaluate the model during the training process, and the multilayer perceptron neural network (MLPNN) classifier performed best in testing the dataset's accuracy and evaluating the model's best performance. Then, a boosting ensemble learning approach and a combination of traffic accident factors improve the experiment's performance. According to this experiment, the results show that this study uses the Pearson Chi-square feature selection method to select important traffic factor combinations, and the boosting method indeed helps improve the effectiveness of the construction of the traffic accident model. Finally, the experimental results of the NN-MLP model have a correct rate of 77% and AUC is 78.7%. In constructing the model, it was found that the degree of injury, the part of the vehicle hit, the type of accident, the leading cause, the type of vehicle, and the period of the accident were the main factors causing dangerous traffic accidents. Doi: 10.28991/HIJ-2024-05-01-012 Full Text: PDF

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Section: Traffic Accident Factors To Explorementioning

confidence: 99%

Section: Traffic Accident Factors To Explorementioning

confidence: 99%

Section: Traffic Accident Factors To Explorementioning

confidence: 99%

See 1 more Smart Citation

Using Multilayer Perceptron Neural Network to Assess the Critical Factors of Traffic Accidents

Ruangkanjanases,

Sivarak,

Weng

et al. 2024

HighTech. Innov. J.

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Traffic Accident Risk Prediction of Tunnel Based on Multi-Source Heterogeneous Data Fusion

Wang,

Liu,

et al. 2024

IEEE Access

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In order to improve the prediction accuracy, this paper proposes a traffic accident risk prediction method of tunnel based on multi-source heterogeneous data fusion. Firstly, the feature extraction and coding model based on Gabor cloud image is constructed, and the unstructured cloud image is enhanced by amplitude feature level and then encoded by fusion. Secondly, the long sequence formed after the sample concatenation of the multi-source heterogeneous data of tunnel is used as the input of the prediction model GRU (Gate Recurrent Unit), and AdaBoost (Adaptive Boosting) is introduced to learn the prediction results to further improve the robustness of the prediction model. The experimental results show that the proposed GRU-AdaBoost model achieves the prediction accuracy of 84.59% when the data volume is 3 years and the time interval is 5 weeks. When the data volume is 1 year and the time interval is 3 weeks, the prediction accuracy can reach 80.85%, which is 9.61% higher than traditional prediction models.

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A Review of Road Traffic Accident Prediction Methods

Cited by 2 publications

References 0 publications

Using Multilayer Perceptron Neural Network to Assess the Critical Factors of Traffic Accidents

Using Multilayer Perceptron Neural Network to Assess the Critical Factors of Traffic Accidents

Traffic Accident Risk Prediction of Tunnel Based on Multi-Source Heterogeneous Data Fusion

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