Prediction of Car Accidents Using a Maximum Sensitivity Neural Network

Contreras, Erika; Torres-Treviño, Luis M.; Torres, Francisco

doi:10.1007/978-3-319-73323-4_9

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…In order to provide a decent prediction for traffic collisions in metropolitan areas of Nuevo León, Contreras et al utilized an innovative ANN model. e programming feature of Scilab software has been used in this analysis to verify the highest sensitivity on the expected neural network [53]. Amin used the backpropagation ANN approach to investigate gender characteristics of older driver accidents and model the variables of their accidents and finally illustrated that journey purpose was the highest contributor factor of accident risk for older drivers and lighting condition was the second most important factor [54].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Urban Traffic Accident Analysis and Prediction Using Logit and Machine Learning-Based Pattern Recognition Models

Gilani

Hosseinian

Ghasedi

et al. 2021

Mathematical Problems in Engineering

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Modeling the severity of accidents based on the most effective variables accounts for developing a high-precision model presenting the possibility of occurrence of each category of future accidents, and it could be utilized to prioritize the corrective measures for authorities. The purpose of this study is to identify the variables affecting the severity of the injury, fatal, and property damage only (PDO) accidents in Rasht city by collecting information on urban accidents from March 2019 to March 2020. In this regard, the multiple logistic regression and the pattern recognition type of artificial neural network (ANN) as a machine learning solution are used to recognize the most influential variables on the severity of accidents and the superior approach for accident prediction. Results show that the multiple logistic regression in the forward stepwise method has R2 of 0.854 and an accuracy prediction power of 89.17%. It turns out that the accidents occurred between 18 and 24 and KIA Pride vehicle has the highest effect on increasing the severity of accidents, respectively. The most important result of the logit model accentuates the role of environmental variables, including poor lighting conditions alongside unfavorable weather and the dominant role of unsafe and poor quality of vehicles on increasing the severity of accidents. In addition, the machine learning model performs significantly better and has higher prediction accuracy (98.9%) than the logit model. In addition, the ANN model’s greater power to predict and estimate future accidents is confirmed through performance and sensitivity analysis.

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Section: Introductionmentioning

confidence: 99%

Data-Driven Urban Traffic Accident Analysis and Prediction Using Logit and Machine Learning-Based Pattern Recognition Models

Gilani

Hosseinian

Ghasedi

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…Afterward, they categorized the collision into three levels of severity, property damage, injury, and death, then different structures were built using the artificial neural network, and eventually the model was validated using new data, and the results of the optimal network parameters showed a high accuracy of the neural network in building the model. E. Contreras et al [34] utilized a model by using ANN to predict traffic accidents in urban zones of Nuevo León city. In this study Scilab development software was used to validate the maximum sensitivity of intended Neural Network.…”

Section: Previous Studiesmentioning

confidence: 99%

Prediction and Analysis of the Severity and Number of Suburban Accidents Using Logit Model, Factor Analysis and Machine Learning: A case study in a developing country

2021

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The purpose of this study is to investigate and determine the factors affecting vehicle and pedestrian accidents taking place in the busiest suburban highway of Guilan Province located in the north of Iran and provide the most accurate prediction model. Therefore, the effective principal variables and the probability of occurrence of each category of crashes are analyzed and computed utilizing the factor analysis, logit, and Machine Learning approaches simultaneously. This method not only could contribute to achieving the most comprehensive and efficient model to specify the major contributing factor, but also it can provide officials with suggestions to take effective measures with higher precision to lessen accident impacts and improve road safety. Both the factor analysis and logit model show the significant roles of exceeding lawful speed, rainy weather and driver age (30–50) variables in the severity of vehicle accidents. On the other hand, the rainy weather and lighting condition variables as the most contributing factors in pedestrian accidents severity, underline the dominant role of environmental factors in the severity of all vehicle-pedestrian accidents. Moreover, considering both utilized methods, the machine-learning model has higher predictive power in all cases, especially in pedestrian accidents, with 41.6% increase in the predictive power of fatal accidents and 12.4% in whole accidents. Thus, the Artificial Neural Network model is chosen as the superior approach in predicting the number and severity of crashes. Besides, the good performance and validation of the machine learning is proved through performance and sensitivity analysis.

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“…The study concluded that the analysis and prediction that used neural networks were better than R regression technique. A recent study was done by (Contreras, Torres-Treviño, & Torres, 2018) which aimed to predict car accidents using the maximum sensitivity of the neural network was advanced, trained and verified using the Scilab development program. The result was concluded with the neuronal network of the maximum sensitivity in that it was possible to predict the occurrence of events weighting them by the times in which they were presented in the historical data.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Prediction of Traffic Accident Severity Using Data Mining Techniques in Ibb Province, Yemen

Hazaa¹,

Saad²,

Alnaklani³

2019

IJSECS

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Traffic accidents are the leading causes beyond death; it is the concern of most countries that strive for finding radical solutions to this problem. There are several methods used in the process of forecasting traffic accidents such as classification, assembly, association, etc. This paper surveyed the latest studies in the field of traffic accident prediction; the most important tools and algorithms were used in the prediction process such as Backpropagation Neural Networks and the decision tree. In addition, this paper proposed a model for predicting traffic accidents based on dataset obtained from the Directorate General of Traffic Statistics, Ibb, Yemen.

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Prediction of Car Accidents Using a Maximum Sensitivity Neural Network

Cited by 5 publications

References 8 publications

Data-Driven Urban Traffic Accident Analysis and Prediction Using Logit and Machine Learning-Based Pattern Recognition Models

Data-Driven Urban Traffic Accident Analysis and Prediction Using Logit and Machine Learning-Based Pattern Recognition Models

Prediction and Analysis of the Severity and Number of Suburban Accidents Using Logit Model, Factor Analysis and Machine Learning: A case study in a developing country

Prediction of Traffic Accident Severity Using Data Mining Techniques in Ibb Province, Yemen

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