Symbolic Regression for Precrash Accident Severity Prediction

2014 ◽  
pp. 133-144 ◽  
Author(s):  
Andreas Meier ◽  
Mark Gonter ◽  
Rudolf Kruse
Keyword(s):  
Symbolic Regression ◽  
Severity Prediction ◽  
Accident Severity

scholarly journals Accident Severity Prediction Using Data Mining Methods

2019 ◽  
pp. 528-536 ◽  
Author(s):  
S. Ramya ◽  
SK. Reshma ◽  
V. Dhatri Manogna ◽  
Y. Satya Saroja ◽  
G. Sanjay Gandhi
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Analytical Data ◽  
Developed Countries ◽  
Influential Factors ◽  
Living Environment ◽  
Severity Prediction ◽  
Accident Severity

The smart city concept provides opportunities to handle urban problems, and also to improve the citizens’ living environment. In recent years, road traffic accidents (RTAs) have become one of the largest national health issues in the world and it is leading cause for deaths. The burden of road accident casualties and damage is much higher in developing countries than in developed countries. Many factors (driver, environment, vehicle, etc.) are related to traffic accidents, some of those factors are more important in determining the accident severity than others. The analytical data mining solutions can significantly be employed to determine and predict such influential factors among human, vehicle and environmental factors. In this research, the classification technique i.e., Random forest algorithm is used to identify relevant patterns and for classifying the type of accident severity of various traffic accidents with the help of influential environmental features of RTAs that can be used to build the prediction model. This technique was tested using a real dataset. A decision system has been built using the model generated by the Random Forest technique that will help decision makers to enhance the decision making process by predicting the severity of the accident.

Traffic accident severity prediction based on oversampling and CNN for imbalanced data

2021 ◽  
Author(s):  
Anqi Shangguan ◽  
Lingxia Mu ◽  
Guo Xie ◽  
Chenglan Wang ◽  
Yang Jing ◽  
...  
Keyword(s):  
Traffic Accident ◽  
Imbalanced Data ◽  
Severity Prediction ◽  
Accident Severity

scholarly journals Prediction for Traffic Accident Severity: Comparing the Bayesian Network and Regression Models

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Fang Zong ◽  
Hongguo Xu ◽  
Huiyong Zhang
Keyword(s):  
Bayesian Network ◽  
Traffic Safety ◽  
Regression Models ◽  
Traffic Accident ◽  
Goodness Of Fit ◽  
Study Results ◽  
Severity Prediction ◽  
Accident Management ◽  
Modeling Techniques ◽  
Accident Severity

The paper presents a comparison between two modeling techniques, Bayesian network and Regression models, by employing them in accident severity analysis. Three severity indicators, that is, number of fatalities, number of injuries and property damage, are investigated with the two methods, and the major contribution factors and their effects are identified. The results indicate that the goodness of fit of Bayesian network is higher than that of Regression models in accident severity modeling. This finding facilitates the improvement of accuracy for accident severity prediction. Study results can be applied to the prediction of accident severity, which is one of the essential steps in accident management process. By recognizing the key influences, this research also provides suggestions for government to take effective measures to reduce accident impacts and improve traffic safety.

scholarly journals An Alternative Method for Traffic Accident Severity Prediction: Using Deep Forests Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Jing Gan ◽  
Linheng Li ◽  
Dapeng Zhang ◽  
Ziwei Yi ◽  
Qiaojun Xiang
Keyword(s):  
Machine Learning ◽  
Traffic Safety ◽  
Traffic Accident ◽  
Learning Algorithm ◽  
Sustainable Transportation ◽  
Machine Learning Algorithm ◽  
Statistical Regression ◽  
Road Traffic Safety ◽  
Severity Prediction ◽  
Accident Severity

Traffic safety has always been an important issue in sustainable transportation development, and the prediction of traffic accident severity remains a crucial challenging issue in the domain of traffic safety. A huge variety of forecasting models have been proposed to meet this challenge. These models gradually evolved from linear to nonlinear forms and from traditional statistical regression models to current popular machine learning models. Recently, a machine learning algorithm called Deep Forests based on the decision tree ensemble has aroused widespread concern, which was proposed for the first time by a research team of Nanjing University. This algorithm was proved to be more accurate and robust in comparison with other machine learning algorithms. Motivated by this benefit, this study employs the UK road safety dataset to propose a novel method for predicting the severity of traffic accidents based on the Deep Forests algorithm. To verify the superiority of our proposed method, several other machine learning algorithm-based perdition models were implemented to predict traffic accident severity with the same dataset, and the prediction results show that the Deep Forests algorithm present good stability, fewer hyper-parameters, and the highest accuracy under different level of training data volume. It is expected that the findings from this study would be helpful for the establishment or improvement of effective traffic safety system within a sustainable transportation system, which is of great significance for helping government managers to establish timely proactive strategies in traffic accident prevention and effectively improve road traffic safety.

scholarly journals RFCNN:Traffic Accident Severity Prediction based on Decision Level Fusion of Machine and Deep Learning Model

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Mubariz Manzoor ◽  
Muhammad Umer ◽  
Saima Sadiq ◽  
Abid Ishaq ◽  
Saleem Ullah ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Decision Level ◽  
Severity Prediction ◽  
Accident Severity ◽  
Decision Level Fusion ◽  
Deep Learning Model ◽  
Level Fusion

scholarly journals Maximising Accuracy and Efficiency of Traffic Accident Prediction Combining Information Mining with Computational Intelligence Approaches and Decision Trees

2014 ◽  
Vol 4 (1) ◽  
pp. 31-42 ◽  
Author(s):  
Tatiana Tambouratzis ◽  
Dora Souliou ◽  
Miltiadis Chalikias ◽  
Andreas Gregoriades
Keyword(s):  
Traffic Accident ◽  
Prediction Models ◽  
Scatter Search ◽  
Principal Component ◽  
Correct Prediction ◽  
Information Mining ◽  
Severity Prediction ◽  
Accident Severity ◽  
Republic Of Cyprus ◽  
Selection Of

Abstract The development of universal methodologies for the accurate, efficient, and timely prediction of traffic accident location and severity constitutes a crucial endeavour. In this piece of research, the best combinations of salient accident-related parameters and accurate accident severity prediction models are determined for the 2005 accident dataset brought together by the Republic of Cyprus Police. The optimal methodology involves: (a) information mining in the form of feature selection of the accident parameters that maximise prediction accuracy (implemented via scatter search), followed by feature extraction (implemented via principal component analysis) and selection of the minimal number of components that contain the salient information of the original parameters, which combined bring about an overall 74.42% reduction in the dataset dimensionality; (b) accident severity prediction via probabilistic neural networks and random forests, both of which independently accomplish over 96% correct prediction and a balanced proportion of under- and over-estimations of accident severity. An explanation of the superiority of the optimal combinations of parameters and models is given, as is a comparison with existing accident classification/prediction approaches

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