From the preceding era, accidents caused by road traffic have emerged as a widespread difficulty. The currently published “global status report on road safety” highlights that 50 million people suffered and 1.35 million deceased in 178 countries due to road mishaps and that goes down even more severely in under-developed countries [1] (Fig. 1). A road traffic accident (RTA) does not occur at random. Its complexity encompasses the interconnections of the different traits of the driver, vehicle, road, and environmental factors. Substantial developments have therefore been undertaken in the field of accident analysis, especially when it comes to the prevention of injury and modeling of accident prediction. Traditionally, the enormous mass of research [2] relevant to accident evaluations is based on different forms of regression modeling with the mainly concerned with accident occurrence rather than on the estimation of accident intensity. Moreover, prior research employing computational modeling approaches [3–5], shows the unpredictable effects due to the socio-economic circumstances of a specific location using their site-specific accident data.

Many prevailing studies [6,7] are somewhat constrained by the problem of limited dataset availability and model over or underfitting due to the usage of single-level classification modeling. The proposed WMV hybrid scheme is trained and tested over multiple accident datasets through multi-level hybrid modeling, which makes it a well-fitted approach for generalizing similar data to that on which it was trained. Hence producing more accurate outcomes. Moreover, a hybrid approach to combine single classifiers has occasionally been employed except in a hierarchical structure of majority voting schemes. Besides this, in state of the art approaches are incorporate the ordinary parameters generating typical prediction accuracy instead of using fine hyper-parameter tuning. Further, limited evaluation measures have been practiced for results comparison like percentage accuracy and root mean squared error only. Hence, revealing few discriminating factors for correct class prediction. So, the proposed research work adds novelty in accident analysis by implementing a well-fitted multi-level hybrid approach which is trained and tested over multiple accident datasets to develop a more generic framework to address the weaknesses in prior studies. It critically considers the impact of road traffic accidents in several levels of casualties as a multi-classification problem rather than modeling the frequency of crashes on a specified section over a long epoch. As a result, the level of accident severity with limited injuries is stated as “Slight,” more injuries as “Serious” and death as “Fatal” severity. In assessing accidents with such comprehensive complexity, essential dynamics in highly predictor variables are identified while the relevance between predictor variables and accident characteristics is consistent when the final prediction is rendered. Moreover, the weighted majority voting (WMV) [8] scheme with parallel structure provides better accuracy as compared with prevailing cascading methodologies that don't reveal reasonable accuracy in terms of “Casualty Severity” prediction. This paper improves the preceding effort in road accident data analysis by considering the strong relationships between accident characteristics and different “Casualty Severity” levels of a particular RTA. In our study, we used general and unbalanced datasets of road accidents from different accident repositories to make comparisons of numerous sophisticated methods to solve a multiple classification problem.

The major contribution of the proposed approach are as follows: 1.

It assesses accident casualty severity level instead of accident frequency count.

The remaining sections of this article are arranged in the following way. The critical analysis of existing state-of-the-art methods is described in Section 2. The detailed methodology of the proposed WMV hybrid scheme is presented in Section 3. The performance assessment of the proposed WMV hybrid scheme is presented in Section 4 alongside a thorough analysis of the research results. A brief conclusion of the proposed WMV hybrid scheme followed by future directions is discussed in Section 5.

Vast investigative state-of-the-art approaches have been used to examine the consequences of several possible causes that affect the degree of injuries caused by traffic collisions. Similarly, statistical and traditional classification methods were used to assess the severity of an accident's injuries. To evaluate the relationships between predictive variables (significant risk variables) and the outcome variable (level of injury), conventional regression techniques have prevailed over other models.

Research work on characterization and severity estimation of traffic accidents in Spain is also conducted by [9], which builds predictive models using naive Bayes, gradient method with boosting trees, and deep machine learning approach. The comparative study of multiple outcomes reveals that the deep learning algorithm outperforms other methods in statistical measures. However, comparing regression models to deep strategies becomes less suitable since deep learning-based frameworks involve substantial scale datasets for learning and fine-tuning of hyperparameters [10] develops a hybrid-based approach to forecast the magnitude of the RTA dataset. The k-means clustering is being used in the study to aggregate crash datasets based on their similarities, and the random forest is being used to group road accident factors into intensity parameters, which increases class accuracy results for logistic regression, random forest, support vector machine (SVM), and k-nearest neighbor (KNN). [11] performed the exploration of traffic violation severity by defining the link among driver sex, age, years of driving, vehicle type, and traffic offense severity using bayesian network (BN), cumulative logistic regression (CLR), and neural network (NN) models. The performance comparison indicates the Bayesian network's performance as higher than other utilized approaches. Due to the limited data usage, the consequences of variables like climate and road conditions and analysis of the relation between traffic violations and road accidents for road accident predictions have not been taken into account. For identifying the determinants of road accidents and estimating the extent of road accidents, [12] applies different classification algorithms including J48, ID3, CART, and Naïve Bayes. The outcome of the analysis shows that fatal accidents occur during rainy weather conditions that drive at midnight and serious accidents occur in foggy weather conditions in one-lane roads. The performance comparison indicates the predictability of J48 as higher than that of other categorization methods. Nevertheless, the modeling approach to ensembles individual statistical methods can further improve the crash seriousness prediction by integrating utilized learning models. The study [13] evolved macro-level collision prediction models utilizing decision tree regression (DTR) models to investigate pedestrian and bicycle collapse. The DTR models revealed major predictor variables in three broad categories: traffic, road, and socio-demographic characteristics. Furthermore, spatial predictor variables of neighboring crashes are considered along with the targeted crashes in both the DTR model's spatial and aspatial DTR models. The model comparison results revealed that the prediction accuracy of the spatial DTR model was higher than the aspatial DTR model. However, specific techniques (i.e., bagging, random forest, and gradient boosting) can be used to further increase the predictive performance of DTR models as they are known to be slow learners. The research study of [14] categorizes the severity of an accident into four types: deadly, grievous, simple damage, and motor collision. The severity of an accident is determined through the Decision Tree, k-nearest neighbors (KNN), naïve Bays, and adaptive boosting algorithms of accidents in Bangladesh. Results found that the number of accidents gets increased based on the condition of surface effect features and at rush hour (06–18) accident rate is very high as compared to other times. Among these four methods, healthy performance is attained by AdaBoost. Moreover, the precise parameters of hyper tuning of utilized statistical learning models can advance their performance. The research work of [15] utilizes the traffic and hazard information from a simulation experiment for each modeling stage to train the backpropagation neural network system. The model is a two-staged framework with the first stage identifies risk and no-risk status, and the second stage identifies high-risk and low-risk status. However, the simulation can not be completely optimized without real traffic data and better calibration. To predict the severity of the crash injury, a two-layer “Stacking Framework” is proposed by [16]. The first category incorporates the Random Forest, GBDT, and AdaBoost approach and the additional category achieves an accident injury level classification relying on a logistic regression model. The calibration phase automates different model specifications through a systematic grid search method. In association with many state-of-the-art approaches, the performance of the stacking model is healthier demonstrated by its precision and recall metric. Nevertheless, improving the quality of the accident datasets still requires further consideration to improve crash severity. [17] employed and compared several statistical learning techniques including Regression of Logistics, Random Forest, Adaptive Regression Multivariates, and the Support Vector Machines as well as the Bayesian neural network to deal with binary classification problems. An imbalanced high-resolution database of road accidents in Austria is used to analyze the consequences of 40 different incident variables. Findings showed that the tree-based ensemble is better than classical approaches such as logistic regression. The conclusions, however, support a compromise between accuracy and sensitivity inherent from the context of the inherently uneven existence of the data sets which challenge and complicate the study of the data sets. Their emphasis has been on investigating driver and pedestrian collisions, with little attention paid to the impact of machine learning precision in properly identifying major risks causing traffic injuries. Because of the current increase in accident frequency, there is a tremendous need for expanding road safety preventive research at this stage. As a result, we attempted to create a new hybrid system to characterizing traffic crash severity by integrating or coordinating “Multinomial Logistic Regression” MLR and “Multilayer Perceptron” MLP classifiers with a weighted majority voting scheme, which yields impressive prediction performance in road accident analysis. It offers implicit classification integration to obtain The findings of this research experiment do provide an understanding of the possible trigger factors that lead to traffic injury accidents. By determining the risk factors, these results will assist transportation institutions and police forces in reducing the serious or fatal injuries involved with traffic collisions. As a result, policymakers may enact new laws or upgrade road networks to reduce deadly or serious traffic incidents. As a result, the overall road collision casualties will be reduced. Moreover, this research work has an alliance with topical application fields as well [18–21].

To identify the exact conditions related to particular casualty severity and to improve road safety, a multi-level hybrid framework of the WMV scheme is proposed to predict the accident casualty severity level of a particular RTA. The WMV scheme is designed with a parallel structure by integrating individually built classifiers into a hybrid system. The main concentration is on the impact of road, environment, and vehicle-related aspects for categorizing the casualty severity. The proposed WMV hybrid scheme consists of different phases as shown in Fig. 2. The major steps of the proposed WMV hybrid scheme are as follows: 1.

Acquiring training examples of the multiple datasets.

The proposed approach considers accident severity analysis by observing the associations between the accident attributes and uniting the prediction decisions made by individually developed supervised learning algorithms. It finds out the best classification approach which can make an impact on overall classification accuracy for the “Casualty Severity” prediction. A software tool must be selected to exercise the utilization of distinct machine learning algorithms for different phases of casualty severity analysis. The software tool selected for this research study is “Eclipse JAVA SDK”. Classifiers implementation, validation, evaluation, and analysis are being performed in “JAVA” with “WEKA JAR 3.8”. WEKA comprises algorithms for data pre-processing, classification, regression, clustering, association rules, and visualization.

To learn high-level representations from the data and classifying the injury severity of road traffic accidents, this research proposes the implementation of Multi-Model network architectures including two distinct machine learning classifiers i.e., MLR, MLP which are trained and implemented using the attributes selected by PCA and CFS using 10-fold cross-validation technique.

The proposed approach analyzes the RTA records to discover the underlying patterns responsible for a particular type of causality severity that occurs in road traffic accidents. Individually designed prediction models and proposed WMV Hybrid model predict the unknown “Casualty Severity” of an accident from a selected accident recordset. Performance comparison indicates the proposed WMV Hybrid classifier as the best prediction approach due to its enhanced evaluation statistics including precision and prediction accuracy as compared to individual classifiers on selected accident datasets. The results of the proposed WMV hybrid model support the road safety policymakers for rendering their decisions in the identification of the most critical aspect related to “Casualty Severity”. Finally, the consequences of this research provide the prospective study related to accident severity analysis using hybrid machine learning techniques, and different security issues [48–51] particularly in the perspective of highway safety. A possible future work direction is to further expand the current research work using deep learning approaches like recurrent neural networks (RNN) and convolutional neural networks (CNN) that require learning at different layers with substantial accident data to get the profound insight analysis of potential risk factors of a road accident. This has the potential to be a useful method for estimating the seriousness of injuries in road collisions.