Analysis of factors affecting the severity of crashes in urban road intersections

Mussone, Lorenzo; Bassani, Marco; Masci, Pietro

doi:10.1016/j.aap.2017.04.007

Cited by 77 publications

(45 citation statements)

References 35 publications

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“…A common method is to use the collision severity as a dependent variable, the driver, road, weather, meteorology and other characteristics as independent variables. They usually use a binary logit or probit model with two levels of severity traffic accidents, or use the multinomial logit model to predict multiple levels of severity traffic accidents [10] . Unlike these modeling methods that rely on fixed rules, machine learning training computers find the logic inherent in data from the data through "training" [14] .…”

Section: Discussionmentioning

confidence: 99%

“…The remaining 25% of the data were treated as testing data. According to the methods previously studied, this study determines the variables method is assess how output changes by varying input variable values one by one [9][10] .…”

Section: Land Use Random Forest Modelmentioning

confidence: 99%

“…The R software will output the neural network structure diagram through the "plot" function. The parameters were selected according to the method of Mussone et al, and 10 neurons in the hidden layer were set [10] . And by using the algorithm, the weight matrix of the model and the visualization of the importance of each variable are obtained.…”

Section: The Back-propagation Neural Network Modelmentioning

confidence: 99%

“…And by using the algorithm, the weight matrix of the model and the visualization of the importance of each variable are obtained. Sensitivity analysis was performed by changing the neurons for parameters (7)(8)(9)(10)(11)(12)(13)(14).…”

Section: The Back-propagation Neural Network Modelmentioning

confidence: 99%

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Meteorological Variables and Prediction of Road Traffic Accident Severity in Suzhou city of Anhui Province of China

Liang

Zhang

Yao

et al. 2020

Preprint

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Background: The prediction of the severity of traffic accidents is concerned by researchers and law enforcement. In order to simulate the relationship between road severity results and meteorological factors, a large number of models have been proposed. This study purpose is to conduct a machine learning model to investigate the impact of meteorological variables on the severity of road traffic accidents. Methods: Using data from the 2007 and 2008 -2017 the Traffic Police Detachment of the Public Security Bureau of Suzhou, 7,795 traffic accidentswere included in this study. We attempted to use a random forest model to convey the nonlinear relationship between meteorological variables and the severity of traffic accidents, and to compare the prediction accuracy of the neural network model. The model is constructed by the randomForest package and the neuralnet package in the R software. 75% of the training samples were divided from the data to establish a prediction model, and the remaining 25% of the test samples were used for testing. In addition, in order to understand the accuracy of the model prediction, the predicted results were calculated and compared with the actual results. Results: In the random forest model, the most optimal mtry parameter value was 5, the number of decision trees is 400. The weight of wind direction, atmospheric pressure and temperature might be higher than other variables. The OOB (out of bag) estimate of error rate was 51.09%, and the error rate for general traffic accident prediction is the lowest (45.97%). Similarly, in the neural network model, the calculated error rate is 61.01%, with the lowest error rate for minor traffic accidents (35.84%). Conclusions: The results of this study show that how using meteorological data predicts the severity of a traffic accident with relative accuracy, and the random forest model may be more suitable than the neural network model. Research and application of machine learning algorithms in the field of traffic accidents should be further explored.

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Section: Discussionmentioning

confidence: 99%

Section: Land Use Random Forest Modelmentioning

confidence: 99%

Section: The Back-propagation Neural Network Modelmentioning

confidence: 99%

Section: The Back-propagation Neural Network Modelmentioning

confidence: 99%

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Meteorological Variables and Prediction of Road Traffic Accident Severity in Suzhou city of Anhui Province of China

Liang

Zhang

Yao

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The most important of these studies are as follows. A Study was done by (Mussone, Bassani, & Masci, 2017), which aimed at determining the most important factors that affect the occurrence of accidents prediction using environmental variables and movement variables using the back-propagation network and generalized linear mixed model. The study concluded that BPNN scored the best performance of GLMMs.…”

Section: Back-propagation 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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Discrete Particle Swarm Optimization Based Filter Feature Selection Technique for the Severity of Road Traffic Accident Prediction

Haruna¹,

Sallehuddin²,

Radzi³

2019

Advances in Intelligent Systems and Computing

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Analysis of factors affecting the severity of crashes in urban road intersections

Cited by 77 publications

References 35 publications

Meteorological Variables and Prediction of Road Traffic Accident Severity in Suzhou city of Anhui Province of China

Meteorological Variables and Prediction of Road Traffic Accident Severity in Suzhou city of Anhui Province of China

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

Discrete Particle Swarm Optimization Based Filter Feature Selection Technique for the Severity of Road Traffic Accident Prediction

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