Accident Severity Prediction Research Articles

Discovering the underground coal mining accident patterns in Spain from 2003 to 2021: Insights through machine learning techniques

The safety of underground coal mining has always been a global concern, involving the stable supply of energy and stakes in miners’ lives. Lessons learned from historical accidents and transforming into practical experience help reduce the quantity and severity of accidents. In this study, six ensemble learning techniques, including AdaBoost, Extra Trees, GBDT, LightGBM, Random Forest, and XGBoost, were used to investigate the correlation between accident-causing factors and severity. Firstly, 39487 underground coal mine accidents data was obtained from Spain, variables were categorized and coded. To address the extreme class imbalance, a new dataset (2468 cases) was obtained by data sampling from the original database. Subsequently, the new dataset was randomly divided into training sets (75% of the data) and test sets (25% of the data), then the hyperparameters of each model were optimized and configured. Thirdly, the models’ performance was evaluated on the test data by five metrics (accuracy, Cohen’s Kappa, precision, recall, and F1). Finally, accident patterns were derived from the identified variables along with preventive strategies. Results show that tree-based ensemble learning model performs better compared to the boosting model, and the relative importance of seven variables were determined, where previous cause (PC) and material agent (MA) are the most important factors, followed by the miner’s physical activity (PA), age (A), and experience (E), scale (S) and preventive organization (PO) are in the third tier. Furthermore, the type of accident and injury caused by PC were confirmed. Working with hand tools, younger age, lack of experience, small-scale coal mines, and unfit preventive organization increased the risk of accidents. This study not only facilitates the prediction of accident severity but also provides strategies for preventing and mitigating accidents.

Traffic accident severity prediction based on interpretable deep learning model

ABSTRACT Accurately predicting traffic accident severity is crucial for road safety. However, existing studies lack interpretability in revealing the relationship between accident severity and key factors. To address this issue, we propose a new interpretable analytical framework. The framework utilizes XGBoost and SHAP to select effective factors. Then the AISTGCN model is constructed by improving the STGCN through the local attention mechanism to predict the severity of the accident. Finally, DeepLIFT is used to interpret the forecasts and identify key factors. Our experiments using real-world UK accident data demonstrate that our proposed AISTGCN outperforms baseline models in outcome prediction with an accuracy of 0.8772. The computation time was reduced and the reliability of predictions was enhanced through screening for effective factors. Furthermore, DeepLIFT provides more reasonable explanations when explaining accidents of different severity, indicating that vehicle count significantly impacts. Our framework aids in developing effective safety measures to reduce accidents.

An accident severity prediction framework with consider features interaction

Accident severity prediction is increasingly important for preventing and reducing traffic accident losses. However, many studies always ignore the complex relationship between features during feature selection. To improve the prediction accuracy of the accident severity prediction model, this paper considers the interactions between multiple features. First, the feature selection algorithm of Recursive Feature Elimination with Cross-Validation (RFECV) is improved by using Shapley Additive explanations (SHAP) as the feature importance assessment metric. Then, to decrease the time expense of manually finding hyperparameters of the model, the Hunter Prey Optimization (HPO) algorithm is introduced and logistic mapping together with stochastic perturbation is added to it, which makes it easier to skip out of the partial optimum during the optimization search. Finally, the improved HPO algorithm is used to optimize the hyperparameters of the CatBoost model. The US traffic accident dataset is introduced for the validity of the proposed framework. Experimental results show that the proposed framework achieves a prediction accuracy of 96.63%, which is better than other state-of-the-art methods. The high accuracy of the prediction model can help decision-makers develop more rational transportation policies, and this study also proposes some traffic management measures based on the selected features.

Towards efficient and accurate prediction of freeway accident severity using two-level fuzzy comprehensive evaluation

Accurately predicting freeway accident severity is crucial for accident prevention, road safety, and emergency rescue services in intelligent freeway systems. However, current research lacks the required precision, hindering the effective implementation of freeway rescue. In this paper, we efficiently address this challenge by categorizing influencing factors into two levels: human and non-human, further subdivided into 6 and 36 categories, respectively. Furthermore, based on the above factors, an efficient and accurate Freeway Accident Severity Prediction (FASP) method is developed by using the two-level fuzzy comprehensive evaluation. The factor and evaluation sets are determined by calculating the fuzzy evaluation matrix of a single factor. The weight matrix is calculated through the entropy method to compute the final evaluation matrix. Based on the maximum membership principle, the severity of the freeway accident is predicted. Finally, based on the experiments conducted with the traffic accident datasets in China and the US, it is shown that FASP is able to accurately predict the severity of freeway traffic accidents with thorough considerations and low computational cost. It is noted that FASP is the first attempt to achieve freeway accident severity prediction using the two-level fuzzy comprehensive evaluation method to the best of our knowledge.

Building an Accident Severity Prediction Framework Using Deep Learning Approaches Can be a Valuable Tool for Enhancing Safety Measures and Improving Emergency Response Systems

Building an Accident Severity Prediction Framework Using Deep Learning Approaches Can be a Valuable Tool for Enhancing Safety Measures and Improving Emergency Response Systems

BConvLSTM: a deep learning-based technique for severity prediction of a traffic crash

Predicting the severity of crashes has become a significant issue in research on road accidents. Traffic accident severity prediction is essential for protecting vulnerable road users and preventing traffic accidents. For practitioners to identify significant risk variables and set appropriate countermeasures in place, explainability of the forecast is also essential. Most previous research ignores the severity of property loss caused by traffic accidents and cannot differentiate between different levels of fatalities and property loss severity. Additionally, while an understandable structure of deep neural networks (DNN) is significantly lacking in existing works, understanding traditional systems is quite simple. An inability to use structural data when describing forecasting and the many attempts to incorporate neural networks afflict the absence of hidden layers. We propose a Deep Learning (DL) framework for forecasting traffic crash severity to overcome the accident severity prediction. It has three steps to process. Initially, collected input data are cleaned. Data cleaning is performed in a preprocessing step. We conduct experiments on two datasets, A Countrywide (US) Traffic Accident Dataset and UK Road Accident Dataset. The outcomes of the experiments demonstrate that the proposed technique outperformed other approaches and produced the best accuracy.

Improving traffic accident severity prediction using MobileNet transfer learning model and SHAP XAI technique.

Traffic accidents remain a leading cause of fatalities, injuries, and significant disruptions on highways. Comprehending the contributing factors to these occurrences is paramount in enhancing safety on road networks. Recent studies have demonstrated the utility of predictive modeling in gaining insights into the factors that precipitate accidents. However, there has been a dearth of focus on explaining the inner workings of complex machine learning and deep learning models and the manner in which various features influence accident prediction models. As a result, there is a risk that these models may be seen as black boxes, and their findings may not be fully trusted by stakeholders. The main objective of this study is to create predictive models using various transfer learning techniques and to provide insights into the most impactful factors using Shapley values. To predict the severity of injuries in accidents, Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Residual Networks (ResNet), EfficientNetB4, InceptionV3, Extreme Inception (Xception), and MobileNet are employed. Among the models, the MobileNet showed the highest results with 98.17% accuracy. Additionally, by understanding how different features affect accident prediction models, researchers can gain a deeper understanding of the factors that contribute to accidents and develop more effective interventions to prevent them.

Traffic Accident Severity Prediction Based on Data Cleaning and Machine Learning (Random Forest / Xgboost)

Traffic accidents have increasingly become a global concern, significantly affecting lives and economic sustainability. The US Accidents dataset from 2016 to 2023 provides an extensive record of accidents across the United States, containing detailed data and environmental data of these accident. This study aims to harness the potential of this rich database to predict severity level of accidents. Our research predominantly revolved around meticulous data cleaning, ensuring that the dataset's integrity was uncompromised. After preprocessing, the cleaned data was subjected to sophisticated Machine Learning techniques, primarily focusing on the Random Forest and XGBoost algorithms. These models were chosen due to their renowned capability in handling complex datasets and rendering accurate predictions, especially in scenarios laden with multiple variables. Upon application, the models demonstrated impressive efficacy. To validate the reliability and performance of our models, we employed the confusion matrix. This tool provided a clear visualization of the models' accuracy, revealing true positives, false negatives, and other crucial metrics. Furthermore, to enhance prediction outcomes, the Voting Classifier was implemented, combining the strengths of our primary models and consequently elevating the overall accuracy. The Random Forest algorithm exhibited substantial precision, while XGBoost further enhanced prediction accuracy. These findings underline the significant role of advanced data analytics and Machine Learning in comprehending traffic accident dynamics. In conclusion, our study emphasizes that leveraging state-of-the-art Machine Learning techniques on well-curated datasets can substantially improve our understanding and prediction of traffic accident severity. Such insights pave the way for the development of more effective preventive measures and safety protocols, aiming for a safer traffic environment in the future.

Learning spatial patterns and temporal dependencies for traffic accident severity prediction: A deep learning approach

Traffic accidents have a substantial impact on human life and property, resulting in millions of injuries every year. To ensure road safety and enhance the research in this direction, it is necessary to develop methods that can efficiently predict and classify the accident severity. However, traffic accident datasets may contain a large number of features, making it challenging to extract relevant information and patterns from high-dimensional data. Moreover, traffic accidents may be influenced by multiple factors and temporal dependencies, leading to a dynamic impact of each factor on accident severity over time. To address these challenges, we propose a novel deep-learning approach for predicting traffic accident severity. Specifically, we first conduct a thorough data preprocessing step to clean the data and ensure its quality. Then, a Convolutional Neural Network (CNN) is introduced to extract spatial features and patterns from the high-dimensional data, followed by a Bidirectional Long Short-Term Memory network (BiLSTM) to capture the temporal dependencies between various factors that affect traffic accidents. We also implement attention mechanisms to weigh the importance of each feature in the prediction, thereby reducing the impact of noisy or irrelevant data. To evaluate the effectiveness of our approach, we conduct experiments on a real-world traffic accident dataset from two cities. The results demonstrate the practicality and effectiveness of our framework for traffic accident severity prediction, with potential to enhance road safety.

Improving Traffic Accident Severity Prediction Using Convoluted Features and Decision-Level Fusion of Models

Although there have been improvements in traffic safety measures, the frequency of traffic accidents continues to persist. Developing countries experience a significant impact from traffic accidents with respect to fatalities and property damage. Traffic accidents happen for multiple reasons, involving traffic conditions, driving violations, driver misjudgments, and so forth. Severe casualties may lead to fatalities; therefore, accident severity prediction might help reduce the chances of fatalities. This research makes use of a U.S. road accident dataset that contains the most relevant 32 factors related to accidents. For obtaining accurate prediction of traffic accident severity, this research proposes a solution based on an ensemble of random forest and support vector classifiers that is trained using deep convoluted features. Features are extracted from the road accident dataset using a convolutional neural network (CNN). The performance of models using original features and CNN features is analyzed that shows the superiority of convoluted features. Experimental results involving the use of several well-known machine learning models indicate that the proposed model can obtain an accuracy of 99.99% for traffic accident severity prediction. The efficacy of the proposed model is validated against existing state-of-the-art approaches.

Exploring the Utility of Auto-machine Learning in Predicting Traffic Accident Severity in Jordan

The World Health Organization (WHO) estimates that approximately 1.35 million annual fatalities are caused by road traffic accidents, representing a significant global challenge. This study focuses on creating a machine-learning model to forecast traffic accident severity in Jordan. The objective of this study is to help reduce fatalities and economic losses caused by accidents, which can achieve using various machinelearning classifiers, like Decision Trees, Random Forests, Light Gradient Boosting Machines, Extra Trees, Bagging Classifiers, and Gradient Boosting. These models were assessed before and after down-sampling to deal with imbalance in accuracy, balanced accuracy, recall, precision, and F1-score metrics with and without hyper parameter tuning. The study emphasized the importance of advanced analytics in improving road safety measures and reducing accident severity. The researchers analyzed a dataset of 115,148 accidents. Factors considered included traffic volume, environmental conditions, and road geometry features. The data was segmented into urban and rural categories for customized modeling. Down-sampling improved the models' ability to detect injuries and deaths in both urban and rural areas. Hyper parameter tuning offered an additional improvement in balanced recall and F1-score, particularly after down-sampling. In urban areas, the LGBM and Gradient Boosting classifiers showed the most significant gains in recall for minority classes, while in rural areas the Random Forest, Bagging, and Extra Trees classifiers maintained a better balance between precision and recall. All classifiers achieved high accuracy (above 0.97) in both urban and rural areas, so that these models can accurately forecast the severity of accidents, including property damage, fatalities, and injuries. The authors have also recommended incorporating variables of temporal patterns, driver behavior, demographic data, and geographic location into the models of accident prediction to make them more efficient and stronger. Keywords: Accident severity prediction, Auto-machine learning, Accident classifiers, Python, Jordan.

Machine Learning for Road Accident Severity Prediction

Machine Learning for Road Accident Severity Prediction

Predictive Modeling of Urban Traffic Accident Severity in Türkiye's Centennial: Machine Learning Approaches for Sustainable Cities

With their far-reaching implications for public health, urban development, and societal harmony, traffic accidents remain a global challenge. As the Republic of Türkiye marks its 100th year, predicting traffic accident severity assumes critical significance, aligning with the nation's aspirations for urban renewal and sustainable progress. This research harnesses the capabilities of machine learning (ML) to anticipate accident severities, shedding light on the critical roles of specific driver and vehicle characteristics. In-depth evaluation of various ML techniques—spanning from Random Forest (RF) and Gaussian Naive Bayes to k-NN, CatBoostClassifier, LightGBM, and Decision Trees—was undertaken, drawing on an expansive dataset that mirrors a spectrum of traffic situations. The RF algorithm demonstrated superior predictive prowess, with certain variables such as Engine_Capacity_(CC), Age_of_Driver, Age_of_Vehicle, Day_of_Week, and Vehicle_Type emerging as decisive factors in accident outcomes. Beyond highlighting RF's potential in accident severity prediction, the study emphasizes the significance of critical determinants. These insights offer a roadmap for stakeholders to craft specialized interventions, amplify public awareness efforts, and pioneer infrastructural upgrades, culminating in a vision of enhanced road safety. Furthermore, this investigation charts a course for Türkiye to foster a sustainable urban trajectory through informed urban and traffic planning initiatives.

Accident severity prediction modeling for road safety using random forest algorithm: an analysis of Indian highways.

Background: Road accidents claim around 1.35 million lives annually, with countries like India facing a significant impact. In 2019, India reported 449,002 road accidents, causing 151,113 deaths and 451,361 injuries. Accident severity modeling helps understand contributing factors and develop preventive strategies. AI models, such as random forest, offer adaptability and higher predictive accuracy compared to traditional statistical models. This study aims to develop a predictive model for traffic accident severity on Indian highways using the random forest algorithm. Methods: A multi-step methodology was employed, involving data collection and preparation, feature selection, training a random forest model, tuning parameters, and evaluating the model using accuracy and F1 score. Data sources included MoRTH and NHAI. Results: The classification model had hyperparameters 'max depth': 10, 'max features': 'sqrt', and 'n estimators': 100. The model achieved an overall accuracy of 67% and a weighted average F1-score of 0.64 on the training set, with a macro average F1-score of 0.53. Using grid search, a random forest Classifier was fitted with optimal parameters, resulting in 41.47% accuracy on test data. Conclusions: The random forest classifier model predicted traffic accident severity with 67% accuracy on the training set and 41.47% on the test set, suggesting possible bias or imbalance in the dataset. No clear patterns were found between the day of the week and accident occurrence or severity. Performance can be improved by addressing dataset imbalance and refining model hyperparameters. The model often underestimated accident severity, highlighting the influence of external factors. Adopting a sophisticated data recording system in line with MoRTH and IRC guidelines and integrating machine learning techniques can enhance road safety modeling, decision-making, and accident prevention efforts.

Evaluating the effectiveness of machine learning techniques in forecasting the severity of traffic accidents

Traffic accidents pose a significant public safety concern, leading to numerous injuries and fatalities worldwide. Predicting the severity of these accidents is crucial for developing effective road safety measures and reducing casualties. This paper proposes an analytic framework that utilizes machine learning models, including Naive Bayes, Random Forest, Logistic Regression, and Artificial Neural Networks, to predict the severity of traffic accidents based on contributing factors. This study analyzed ten years of UK traffic accident data (2005–2014, N = 2,047,256) to develop and compare different ML models. Results show that the proposed Random Forest and Logistic Regression models achieved an 87% overall prediction accuracy, outperforming Naive Bayes (80%) and Artificial Neural Networks (80%). By employing Random Forest-based feature importance analysis, the study identified Engine Capacity, Age of the vehicle, make of vehicle, Age of the driver, vehicle manoeuvre, daytime, and 1st road class as the most sensitive variables influencing traffic accident severity prediction. Additionally, the suggested RF model outperformed most existing models, attaining a remarkable overall accuracy and superior predictive performance across various injury severity classes. The findings have significant implications for developing efficient road safety measures and enhancing the current traffic safety system. The proposed framework and models can be adapted to various datasets to achieve accurate and effective predictions of traffic accident severity, serving as a valuable reference for implementing traffic accident management and control measures. Future research could extend the proposed framework to datasets containing Casualty Accident information to further improve the accuracy of injury severity prediction.

Accident severity prediction modeling for road safety using random forest algorithm: an analysis of Indian highways

Background: Road accidents claim around 1.35 million lives annually, with countries like India facing a significant impact. In 2019, India reported 449,002 road accidents, causing 151,113 deaths and 451,361 injuries. Accident severity modeling helps understand contributing factors and develop preventive strategies. AI models, such as random forest, offer adaptability and higher predictive accuracy compared to traditional statistical models. This study aims to develop a predictive model for traffic accident severity on Indian highways using the random forest algorithm. Methods: A multi-step methodology was employed, involving data collection and preparation, feature selection, training a random forest model, tuning parameters, and evaluating the model using accuracy and F1 score. Data sources included MoRTH and NHAI. Results: The classification model had hyperparameters ’max depth’: 10, ’max features’: ’sqrt’, and ’n estimators’: 100. The model achieved an overall accuracy of 67% and a weighted average F1-score of 0.64 on the training set, with a macro average F1-score of 0.53. Using grid search, a random forest Classifier was fitted with optimal parameters, resulting in 41.47% accuracy on test data. Conclusions: The random forest classifier model predicted traffic accident severity with 67% accuracy on the training set and 41.47% on the test set, suggesting possible bias or imbalance in the dataset. No clear patterns were found between the day of the week and accident occurrence or severity. Performance can be improved by addressing dataset imbalance and refining model hyperparameters. The model often underestimated accident severity, highlighting the influence of external factors. Adopting a sophisticated data recording system in line with MoRTH and IRC guidelines and integrating machine learning techniques can enhance road safety modeling, decision-making, and accident prevention efforts.

Forecasting road traffic accident using deep artificial neural network approach in case of Oromia Special Zone

Millions of people are dying, and billions of properties are damaged by road traffic accidents each year worldwide. In the case of our country Ethiopia, the effect of traffic accidents is even more by causing injuries, death, and property damage. Forecasting road traffic accident and predicting the severity of road traffic accident contributes a role indirectly in reducing road traffic accidents. This study deals with forecasting the number of accident and prediction of the severity of an accident in the Oromia Special Zone using deep artificial neural network models. Around 6170 road traffic accidents data are collected from Oromia Police Commission Excel data and Oromia Special Zone Traffic Police Department; the dataset consists of accidents in the Special Zone of Oromia Districts (Woredas) from 2005 to 2012 with 15 accidents attributes. 5928 or (80%) of the dataset was used for the training model, and 1482 or (20%) of the dataset was used for the testing model. This study proposed six different neural network architectures such as backpropagation neural network (BPNN), feedforward neural network (FFNN), multilayer perceptron neural network (MLPNN), recurrent neural networks (RNN), radial basis function neural network (RBFNN) and long short-term memory (LSTM) models for accident severity prediction and the LSTM model for a time serious forecasting of number accidents within specified years. The models will take input data, classify accidents, and predict the severity of an accident. Accident predictor GUI has been created using Python Tkinter library for easy accident severity prediction. According to the model performance results, RNN model showed the best prediction accuracy of 97.18%, whereas MLP, LTSM, RBFNN, FFNN, and BPNN models showed the accuracy of 97.13%, 91.00%, 87.00%, 80.56%, and 77.26%, respectively. LTSM model forecasted accident for three years which is 3555 where the actual accident number is 3561. The prediction and forecast result obtained from the model will be helpful in planning and management of road traffic accidents.

Application of machine learning technology for occupational accident severity prediction in the case of construction collapse accidents

Machine learning algorithms are capable of handling complex non-linear problems related to the prediction domain, but further exploration is required for automated, semi-supervised outcome prediction of occupational accidents employing unstructured textual data. It has been demonstrated that the injury severity can be predicted from the equipment, scenario and environmental attributes in the workplace, so this paper aims to enhance text data pre-processing and optimize machine learning algorithms to create an attribute factor-based occupational accident severity prediction framework, mapping characteristic attributes to accident severity categories (i.e., casualties and property damage). The reliability validation of the prediction framework and analysis of critical attribute components are performed using the collapse accidents data in construction engineering as a case study, which is the third most serious occupational problem. The findings indicate that the dataset obtained after addressing the class imbalance issue and improving the text segmentation procedure can be utilized as a training sample to accurately predict injury severity. The accuracy of the prediction model is evaluated in three simulated scenarios, and it can reach 82%, confirming the robust performance of the prediction model based on RF machine learning. Additionally, the outcomes of the measured ranking of feature importance enable the identification of critical attributes that can credibly explain the causal relationships resulting in injury severity findings, and provide managers with accident prevention strategies to minimize occupational injuries and losses.

Predicting traffic crash severity using hybrid of balanced bagging classification and light gradient boosting machine

Accident severity prediction is a hot topic of research aimed at ensuring road safety as well as taking precautionary measures for anticipated future road crashes. In the past decades, both classical statistical methods and machine learning algorithms have been used to predict traffic crash severity. However, most of these models suffer from several drawbacks including low accuracy, and lack of interpretability for people. To address these issues, this paper proposed a hybrid of Balanced Bagging Classification (BBC) and Light Gradient Boosting Machine (LGBM) to improve the accuracy of crash severity prediction and eliminate the issues of bias and variance. To the best of the author’s knowledge, this is one of the pioneer studies which explores the application of BBC-LGBM to predict traffic crash severity. On the accident dataset of Great Britain (UK) from 2013 to 2019, the proposed model has demonstrated better performance when compared with other models such as Gaussian Naïve Bayes (GNB), Support vector machines (SVM), and Random Forest (RF). More specifically, the proposed model managed to achieve better performance among all metrics for the testing dataset (accuracy = 77.7%, precision = 75%, recall = 73%, F1-Score = 68%). Moreover, permutation importance is used to interpret the results and analyze the importance of each factor influencing crash severity. The accuracy-enhanced model is significant to several stakeholders including drivers for early alarm and government departments, insurance companies, and even hospitals for the services concerned about human lives and property damage in road crashes.

Predicting the severity of traffic accidents on mountain freeways with dynamic traffic and weather data

Abstract Traffic accident severity prediction is essential for dynamic traffic safety management. To explore the factors influencing the severity of traffic accidents on mountain freeways and to predict the severity of traffic accidents, four models based on machine learning algorithms are constructed using support vector machine (SVM), decision tree classifier (DTC), Ada_SVM and Ada_DTC. In addition, random forest (RF) is used to calculate the importance degree of variables and the accident severity influences with high importance levels form the RF dataset. The results show that rainfall intensity, collision type, number of vehicles involved in the accident and road section type are important variables influencing accident severity. The RF feature selection method improves the classification performance of four machine learning algorithms, resulting in a 9.3%, 5.5%, 7.2% and 3.6% improvement in prediction accuracy for SVM, DTC, Ada_SVM and Ada_DTC, respectively. The combination of the Ada_SVM integrated algorithm and RF feature selection method has the best prediction performance, and it achieves 78.9% and 88.4% prediction precision and accuracy, respectively.