• <tr id="yyy80"></tr>
  • <sup id="yyy80"></sup>
  • <tfoot id="yyy80"><noscript id="yyy80"></noscript></tfoot>
  • 99热精品在线国产_美女午夜性视频免费_国产精品国产高清国产av_av欧美777_自拍偷自拍亚洲精品老妇_亚洲熟女精品中文字幕_www日本黄色视频网_国产精品野战在线观看 ?

    Profiling Casualty Severity Levels of Road Accident Using Weighted Majority Voting

    2022-08-23 02:16:40SabaAwanZahidMehmoodHassanNazeerChaudhryUsmanTariqAmjadRehmanTanzilaSabaandMuhammadRashid
    Computers Materials&Continua 2022年6期

    Saba Awan,Zahid Mehmood,Hassan Nazeer Chaudhry,Usman Tariq,Amjad Rehman,Tanzila Saba and Muhammad Rashid

    1Department of Software Engineering,University of Engineering and Technology,Taxila,47050,Pakistan

    2Department of Computer Engineering,University of Engineering and Technology,Taxila,47050,Pakistan

    3Department of Electronics,Information and Bioengineering,Politecnico di Milano,Milano,20122,Italy

    4College of Computer Engineering and Sciences,Prince Sattam bin Abdulaziz University,Al-Kharj,11942,Saudi Arabia

    5College of Computer and Information Sciences,Prince Sultan University,Riyadh,Saudi Arabia

    6Department of Computer Engineering,Umm Al-Qura University,Makkah,21421,Saudi Arabia

    Abstract: To determine the individual circumstances that account for a road traffic accident,it is crucial to consider the unplanned connections amongst various factors related to a crash that results in high casualty levels.Analysis of the road accident data concentrated mainly on categorizing accidents into different types using individually built classification methods which limit the prediction accuracy and fitness of the model.In this article, we proposed a multi-model hybrid framework of the weighted majority voting (WMV)scheme with parallel structure, which is designed by integrating individually implemented multinomial logistic regression (MLR) and multilayer perceptron (MLP) classifiers using three different accident datasets i.e., IRTAD,NCDB, and FARS.The proposed WMV hybrid scheme overtook individual classifiers in terms of modern evaluation measures like ROC, RMSE,Kappa rate, classification accuracy, and performs better than state-of-theart approaches for the prediction of casualty severity level.Moreover, the proposed WMV hybrid scheme adds up to accident severity analysis through knowledge representation by revealing the role of different accident-related factors which expand the risk of casualty in a road crash.Critical aspects related to casualty severity recognized by the proposed WMV hybrid approach can surely support the traffic enforcement agencies to develop better road safety plans and ultimately save lives.

    Keywords:Prediction;hybrid framework;severity;class;casualty

    1 Introduction

    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.

    Figure 1:Global status report on road safety 2018

    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 multilevel 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:

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

    b) It uses a multi-level statistical model for supervised learning which is based on multinomial logistic regression(MLR)and multilayer perceptron(MLP)classifiers.

    c) It selects features based on correlations using statistical resampling and dimensionality reduction.

    d) It performs hyperparameter tuning of multi-level models for adequate regulation of the developed classifiers.

    e) It can accurately predict the unknown casualty severity of an RTA.

    f) It uses a generic hybrid framework by integration of individually developed models using the WMV ensemble modeling approach with Parallel structure.

    g) It utilizes knowledge discovery by exploring the individual behavioral characteristics,highway aspects,environmental aspects,and vehicle attributes related to a specific casualty severity.

    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.

    2 Literature Review

    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.Thek-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),andk-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 Na?ve 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 onelane 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), na?ve 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].

    3 Methodology

    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:

    Figure 2:Block diagram of proposed approach of WMV

    1.Acquiring training examples of the multiple datasets.

    2.Performing data preprocessing using;

    a) Co-relation-based feature selection.

    b) Synthetic minority oversampling technique(SMOTE).

    c) Missing value replacement filter.

    d) 10 fold cross-validation with 60%–40%rule of training and testing.

    3.Performing model implementation using;

    a.MLR.

    b.MLP.

    c.Hybrid modeling of WMV.

    4.Performing model evaluation using;

    a.Testing.

    b.Predictions.

    5.Casualty severity type prediction.

    6.Go to step 2 for the next dataset.

    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.

    3.1 Accident Datasets Acquisition

    The performance of the proposed approach is examined using the different datasets whose details are provided in the following subsequent sections:

    3.1.1 IRTAD Dataset

    Firstly,we selected the IRTAD dataset[22]and utilized the accident records from the year 2019 to 2020.It has 11,257 accident records with 26 unique accident features having accident severity classes named “Fatal”, “Slight” and “Serious” injury crashes, and distinct accident characteristics related to road_type,road_user age,gender,seat position plus environmental and weather conditions at the moment of the accident.

    3.1.2 Fatality Analysis Reporting System(FARS)Dataset

    Secondly, we used FARS [23] dataset having each accident record that covers 38 information components characterizing the crash,cars,and the participating persons.The selected dataset includes data concerning the 35,029 records of motor vehicle accidents of the year 2019 to 2020 on the national motorways.

    3.1.3 National Collision Database(NCDB)

    For the performance analysis of the proposed WMV hybrid scheme, a third utilized dataset is NCDB [24], which consists of a total of 28984 accident records with 20 distinct car, driver, and environmental prediction characteristics for the collisions in the complete year 2017.

    3.2 Data Pre-Processing

    As part of the proposed approach,firstly all the accident record features are being incorporated into a distinct data matrix form before using any data mining technique[25].

    3.2.1 Synthetic Minority Oversampling

    To create a balanced dataset that contains the equal representation of each target class,“Resampling”is performed by employing the synthetic minority oversampling technique(SMOTE)[26]as a pre-processing step.

    3.2.2 Filter for Substituting Lost Values

    Lost values may be a communal issue on a larger level in real existence.The accident datasetsIRTAD,FARS,and NCDBcomprised limited entries where quantitative amounts of particular traits are lost.For example,an omitted value in the lightning conditions attributes specifies that there were no street lights present at the time of the crash.In this manner,to bring down the issue of lost values in other attributes,the“Mean substitution-based imputation”approach has been utilized to substitute the lost entries with measurable approximations of the adjacent entries.The selected substitution approach i.e.,“mean substitution-based imputation”[27]figure out the average estimate of the features and custom this average estimate to supply the lost entry.

    3.2.3 Correlation Based Feature Selection

    Furthermore,the pre-processing stage is followed by the correlation-based feature selection(CFS)[28] technique with the Greedy stepwise search [29] approach.The CFS is used to recognize and eliminate unwanted, inappropriate, and repeated features from the accident record.CFS identifies the features that are more significant and potential forecasters for predicting the target attribute.The CFS criterion is defined as follows:

    Thercfkis the average value of all feature classification correlations andrfifjis the average value of all feature-feature correlations.

    3.2.4 Dimensionality Reduction

    Lastly,principal component analysis(PCA)[30]is applied as a pre-processing step to select the set of attributes combinations to reduce the data dimensions.PCA’s adjustment is made by subtracting the variable’s mean from each value.New variables which are called the factors or principal components are constructed as weighted averages of the original variables.Their specific values on a specific row are referred to as the scores.The matrix of scores is referred to as the matrixY.The basic equation of PCA is,in matrix notation,given by:

    wherewis a matrix of coefficients that are determined by PCA with a data matrix asxwhich consists of n observations(rows)onpvariables(columns).

    3.2.5 Statistical Resampling Using K-Fold Cross-Validation

    Besides the strategies connected for dataset handling and classifiers,k-fold cross approval resampling [31] of a dataset is utilized in aggregation.The procedure is utilized to part the input dataset into preparing and test information.Preparing information is utilized to instruct the dataset whereas test information is utilized to assess the trained classifier.We have chosen a number of folds to be 10 as a cross-validation method with 10-folds, partition the input dataset subjectively into 10 identical small-scale divisions.Furthermore,in any cross-validation usingk-fold,commonly one test is utilized as approval data/testing information whereas the remainingk-1 tests of information are used as preparing information.The sampling technique is mathematically expressed as follows:

    wherek: {1,..., N} be an indexing function that indicates the partition to which observationiis allocated by randomization.f∧-k(i)is the fitted function.Typical Choices ofkare 5 to 10.In the casek(i)=iand for theithobservation,the fit is computed using all the data except theithobservation.

    4 Predictive Modelling

    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.

    4.1 Multinomial Logistic Regression Classifier

    The first utilized classifier as part of the predictive modeling stage is the MLR classifier[32].In statistics,MLR is a classification method that generalizes logistic regression to multiclass problems,i.e.,with more than two possible discrete outcomes.It is a model that is used to predict the probabilities of the different possible outcomes of a categorically distributed dependent variable, given a set of independent variables.The objective is to construct as a linear predictor function that constructs a score from a set of weights that are linearly combined with the explanatory variables (features) of a given observation using a dot product,mathematically defined as follows:

    whereXiis the vector of explanatory variables describing observationi,βkis a vector of weights(or regression coefficients) corresponding to outcomek, and score(Xi,k)is the score associated with assigning observationito categoryk.As a part of the model implementation and classification phase,chosen classifier MLR is applied on all three distinct accident datasets to predict our target attribute“Casualty Severity”which is nominal and having three values i.e., “Slight”.“Serious”and “Fatal”.Classification using MLR has been performed with the following parameters:a maximum number of iterations is set to 10 and“Ridge Value”in the“l(fā)og-likelihood”set to“1.0E-8”.

    4.2 Multilayer Perceptron Classifier

    One of the categories of feedforward artificial neural network(ANN)is the multilayer perceptron(MLP)classifier[33].It comprises at least ternary layers of connection:an input tier,a concealed tier,and a turnout tier.But for the input connections, each connection could be a neuron that employs a nonlinear actuation function.Controlled learning practice named“Backpropagation”is employed by MLP for preparing the input data.In case an MLP encompasses a straight actuation work in all neurons, that’s, a direct work that maps the weighted inputs to the outcome of each neuron, at that point direct variable-based calculations show that any number of layers can be diminished to a twolayer input-output classifier.The historically common activation function is the sigmoid function and is mathematically defined by:

    whereyithe output of the ith node(neuron)andviis the weighted sum of the input connections.It is a hyperbolic tangent that ranges from-1 to 1,while the other is the logistic function,which is similar in shape but ranges from 0 to 1.As a second classification model,the MLP classifier is MLP model is applied on accident records from selected accident datasets to predict the outcome i.e., Casualty Severity.MLP classifier performed the classification with the parameter settings as follows:“hidden layers”to be calculated as“a’=(attributes+classes)/2”,“decay”is set to false to increase the learning rate,“normalizeAttributes”is set to“True”that will normalize the attributes,“l(fā)earning rate”is set to 0.3 and“momentum”is set to 0.2.

    4.3 Hybrid Model of Weighted Majority Voting Scheme

    The second stage of the proposed approach employs the hybrid classification approach which is preferably employed because of its better accuracy and precision as compared to an individual classifier.Rendering to the “No Free Lunch Theorem” [34] the finest machine learning technique which is best for any prediction problem doesn’t exist.So,the integration of various classifiers in the form of a hybrid classification model provides better results as specified by[35].Fig.2 illustrates the hybrid modeling architecture for unknown“Casualty Severity”prediction.

    Figure 3:Architecture of the hybrid modeling of the proposed approach

    The major approaches of hybrid modeling being utilized are cascading[36],hierarchical[37],and parallel[38](Fig.3).In cascading approach,the outcome of one classifier is used as feedback for the subsequent classifier to perform the classification.Employing a collection of two-fold classification methods organized by way of a “tree” in class orbit, a hierarchical classification strategy resolves multiple categorical complexities in higher dimensional areas.In contrast to that,the parallel approach receives an identical input for all the selected models and combine their outcome employing specified decision reasoning.As discussed in[39]decision reasoning can be direct which includes average and weighted average of the outcomes or indirect that includes voting, probabilistic, and rank cantered approaches.In the proposed approach, the parallel ensemble approach is being utilized to integrate the individual classifiers with the WMV scheme.The proposed WMV hybrid scheme also performs well in a case where all individual classifiers provide less effective results.A weight factor is assigned to each model.For each model,predicted class likelihoods are accumulated,then its product is taken with the model’s weight, and the average is calculated.Based on these weighted average likelihoods,the class tag is assigned using the mathematical equation as follows:

    whereXSis the attribute function[Ci(X)=j∈S]andSis the collection of distinct target class values.After the classification is performed by individual models,it is a prerequisite to reduce their distinct faults by using a WMV Hybrid classification approach.Henceforth, the hybrid model is created by integrating the individual classifiers that are MLR and MLP.To evaluate the combined effort of all the selected classifiers,the proposed WMV Hybrid model is applied on all three accident datasets to predict our target attribute i.e.,Casualty Severity.A hybrid model is designed using a training dataset with parameter settings as follows:seed value is set to“2”which is a random number seed to be used,“batch size”is set to “100”which is the preferred number of instances to process, this combination rule is termed as“Weighted Majority Voting”.

    4.4 Model Testing and Predictions

    This section provides the details about testing activity that has been performed for each classifier and the WMV Hybrid model for Casualty Severity prediction of road traffic accident datasetsIRTAD,FARS,and NCDBusing the 10 fold cross-validation method.With this method,we produced one data set of every accident dataset which we divided randomly into 10 parts.We used 9 of those parts for training and reserved one-tenth for testing.We repeated this procedure 10 times,each time reserving a different tenth for testing.Through this method, we have performed out-of-sample testing, for assessing that how well each individual and hybrid model generalizes to an independent dataset to obtain the best prediction accuracy.

    4.5 Model Evaluation and Discussions

    After performing the testing and predictions activity,the next phase is to provide the model evaluation with a performance comparison of the proposed framework for Casualty Severity prediction of road traffic accident datasetsIRTAD,FARS,and NCDBusing individual classifiers and the WMV Hybrid model.A quantitative analysis of the results generated by the proposed framework is also presented here.We correspondingly presented the evaluation comparison of the anticipated WMV Hybrid model and individually implemented classification approaches i.e.,MLR and MPL on selected accident datasets.

    4.5.1 Quantitative Result Evaluation

    To begin with the experimental evaluation, we first utilized the empirical assessment measures i.e.,“Precision”,“Recall”,“Classification Accuracy”,“Mean Absolute Error”(MAE),“Root Mean Squared Error”(RMSE),and“Relative Absolute Error”(RAE)for execution assessment.Upon all three accident recordsIRTAD,FARS,and NCDB,the proposed WMV Hybrid method achieves the highest precision and recall rate respectively of 0.894,0.996,and lowest MAE and RMSE of 0.0731,0.2705 which is above the implemented classifiers on the IRTAD dataset as presented in Tab.1.

    Table 1: Performance evaluation of the proposed WMV hybrid scheme on different datasets

    4.5.2 Performance Comparison

    For the subsequent evaluation approach, confusion matrix [40] examination is undertaken to exhibit the accurateness of predicted estimates performed by selected approaches.Along with the prediction accuracy, other evaluation metrics i.e., F-measure and ROC Area and Kappa metric [41]are also being utilized to access the performance of implemented methods.Kappa Rate is a measure of agreement between the predictions and the real outputs.ROC area provides an effective way to choose better classifiers and reject others.A perfect prediction model generates ROC area rate approaches towards 1.It represents the assessment of the total accuracy to the estimated random chance accuracy.Kappa rate larger than 0 indicates that the model performs better than the random chance classifier of the proposed method and individual classifiers for each target class on selected datasets.Although all of the implemented classifiers performed reasonably well, however, the proposed WMV Hybrid classifier outperformed the individual classifiers for the“Casualty Severity”prediction of an RTA.The comparison between the reported metrics using the MLR,MLP,and proposed WMV Hybrid model is presented in Tab.2.As classifiers with the highest prediction accuracy,F-Measure and ROC area are preferred.So,the outcomes of implementing the specified framework indicate that the proposed WMV Hybrid approach attains the uppermost prediction accuracy of 89.0281%,F-measure of 0.942,Roc Area of 0.513,and Kappa Rate of 0.0395 among the implemented classifiers on the IRTAD dataset as presented in Tab.2.

    Table 2: Performance analysis of the proposed approach in terms of performance parameters

    4.5.3 Relative Assessment of the Presented and Prevailing Approaches

    As the third step of experimental evaluation,a relative assessment of the presented and prevailing approaches for accident severity prediction in terms of precision has been specified.In this regard,Tab.3 below projects the comprehensive evaluation.Since the assessment outcomes undoubtedly confirm the ascendance of a specified framework as it overtakes all prevailing methods in terms of prediction for unknown“Casualty Severity”of an RTA.This indicates the validity of the presented WMV Hybrid classifier in terms of accurate prediction of an unknown “Casualty Severity”.The subsequent fine technique of SVG and GMM attains the values of precision as 0.98.Lastly,the flawed approach of the fuzzy decision obtained the lowest precision of 0.61.

    Table 3:The performance comparison of the proposed WMV hybrid scheme with different state-ofthe-art approaches

    4.5.4 Computational Complexity Comparison

    As the final step of experimental evaluation,computational complexity comparison between the proposed WMV Hybrid classifier and prevailing approaches has been carried out.In this regard,Tab.4 below projects the comparative evaluation.The results showed that, while the computation time of adaptive algorithms differs slightly,the proposed approach is carried out through hybrid integration of two individual models MLR and MLP using a weighted majority voting scheme,resulting in more complex results with higher prediction accuracy than previous studies that used individual algorithms.It produces rapid global and precise local optimal findings,displaying a better comprehension of the entire model description, and finally, the feature analysis revealed that non-road-related variables,notably driver variables, are more essential than highway variables.The methodology established in this work can be extended to big data predictive analytics of road accident fatalities and used by traffic policy regulators and traffic safety experts as a rapid tool.

    Table 4:Computational complexity comparison of the proposed WMV hybrid scheme with existing approaches

    4.5.5 Knowledge Representation

    The findings of result evaluation and performance comparisons show that the age group of 18 to 30 years is identified as the most vulnerable age group involved in traffic accidents of various severity levels.Besides, the Type of Vehicle is also found to be an important factor to discriminate among different casualty severities.Mostly 50cc motorcycles are found to be involved in Slight casualty accidents and 500cc motorcycles are identified to be involved in Serious casualty accidents.Moreover,Goods vehicles with 7.5 tons are identified to be involved in fatal accidents.Most of the female drivers are observed to be involved in slight casualty crashes and male drivers are being involved in both serious and fatal casualty accidents.The road type and road surface condition are also found to be distinguishing attributes for predicting the “Casualty Severity”as both attributes show the highest classifier related to target attribute generated weights by MLR.The proposed approach provides a comprehensive analysis and findings of important factors that cause accidents of different severity levels.

    5 Conclusion and Future Work

    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.

    Funding Statement:The authors received no specific funding for this study.

    Conflicts of Interest:The authors declare that they have no conflicts of interest to report regarding the present study.

    亚洲国产精品一区二区三区在线| 亚洲少妇的诱惑av| 久久精品国产综合久久久| 最新中文字幕久久久久| 少妇被粗大的猛进出69影院| 国产精品嫩草影院av在线观看| 亚洲精品一二三| 黄色怎么调成土黄色| 久久99蜜桃精品久久| 少妇的逼水好多| 麻豆乱淫一区二区| 我的亚洲天堂| 亚洲一码二码三码区别大吗| 国产亚洲av片在线观看秒播厂| 国产av国产精品国产| 久久久精品国产亚洲av高清涩受| 90打野战视频偷拍视频| 在线观看人妻少妇| 我要看黄色一级片免费的| 久久久国产一区二区| 国产女主播在线喷水免费视频网站| 久久国产精品男人的天堂亚洲| 9191精品国产免费久久| 日韩精品有码人妻一区| 午夜福利,免费看| 日韩一区二区三区影片| 成人18禁高潮啪啪吃奶动态图| 国产高清不卡午夜福利| 免费观看无遮挡的男女| 亚洲精品视频女| 免费看不卡的av| 国产精品99久久99久久久不卡 | 午夜福利在线免费观看网站| 老女人水多毛片| 下体分泌物呈黄色| 欧美精品国产亚洲| 欧美精品人与动牲交sv欧美| 免费在线观看完整版高清| 美女视频免费永久观看网站| 嫩草影院入口| 99香蕉大伊视频| 久久久久精品人妻al黑| 女人高潮潮喷娇喘18禁视频| 国产精品熟女久久久久浪| 菩萨蛮人人尽说江南好唐韦庄| 亚洲欧美精品自产自拍| 久久这里有精品视频免费| 777久久人妻少妇嫩草av网站| www.熟女人妻精品国产| 国产极品天堂在线| 亚洲精品美女久久av网站| 97在线人人人人妻| 亚洲精品日本国产第一区| 91久久精品国产一区二区三区| 日本av免费视频播放| 在线天堂最新版资源| 丁香六月天网| 国产日韩欧美视频二区| 黄色毛片三级朝国网站| 亚洲精品,欧美精品| 人人妻人人爽人人添夜夜欢视频| 纯流量卡能插随身wifi吗| av卡一久久| 极品人妻少妇av视频| 免费女性裸体啪啪无遮挡网站| 人妻 亚洲 视频| 另类精品久久| 午夜福利影视在线免费观看| 亚洲精品国产一区二区精华液| 亚洲一区二区三区欧美精品| 亚洲精品美女久久久久99蜜臀 | 亚洲av日韩在线播放| 99久久人妻综合| 亚洲av免费高清在线观看| 久久午夜综合久久蜜桃| 亚洲一级一片aⅴ在线观看| 1024视频免费在线观看| 亚洲,欧美,日韩| 女人高潮潮喷娇喘18禁视频| 国产精品免费视频内射| 啦啦啦视频在线资源免费观看| 亚洲av男天堂| 久久99精品国语久久久| 久久久久久久久久久免费av| 男人添女人高潮全过程视频| 久久久久国产精品人妻一区二区| 卡戴珊不雅视频在线播放| 高清av免费在线| 婷婷色综合大香蕉| 成人毛片60女人毛片免费| 不卡视频在线观看欧美| 日日啪夜夜爽| 精品少妇一区二区三区视频日本电影 | 99久久中文字幕三级久久日本| 午夜日本视频在线| 90打野战视频偷拍视频| 少妇被粗大的猛进出69影院| 国产在线视频一区二区| 成人亚洲精品一区在线观看| videosex国产| 男女啪啪激烈高潮av片| 观看av在线不卡| 自拍欧美九色日韩亚洲蝌蚪91| av国产精品久久久久影院| 777久久人妻少妇嫩草av网站| 熟妇人妻不卡中文字幕| 亚洲视频免费观看视频| 成人毛片a级毛片在线播放| 亚洲av中文av极速乱| 婷婷色综合大香蕉| 激情五月婷婷亚洲| 99久久精品国产国产毛片| 欧美 日韩 精品 国产| 热99国产精品久久久久久7| 国产视频首页在线观看| 国产av国产精品国产| 国产人伦9x9x在线观看 | 两个人免费观看高清视频| 久久久久久久久久久免费av| 如日韩欧美国产精品一区二区三区| 国产成人精品婷婷| 亚洲婷婷狠狠爱综合网| 一级a爱视频在线免费观看| 丁香六月天网| 亚洲男人天堂网一区| 黄频高清免费视频| 最新中文字幕久久久久| 精品一区在线观看国产| 精品久久久精品久久久| 丝袜在线中文字幕| 国产午夜精品一二区理论片| 亚洲成色77777| 这个男人来自地球电影免费观看 | 国产一区亚洲一区在线观看| 久久99热这里只频精品6学生| 青春草国产在线视频| 男人添女人高潮全过程视频| 一本一本久久a久久精品综合妖精 国产伦在线观看视频一区 | 女人久久www免费人成看片| 边亲边吃奶的免费视频| 一区二区三区乱码不卡18| 天天操日日干夜夜撸| 国产免费福利视频在线观看| 免费高清在线观看日韩| 免费在线观看完整版高清| 久久这里有精品视频免费| 国产亚洲av片在线观看秒播厂| 在线观看www视频免费| 亚洲欧美精品综合一区二区三区 | 国产成人aa在线观看| 国产精品熟女久久久久浪| 午夜老司机福利剧场| 91精品国产国语对白视频| 综合色丁香网| 亚洲av福利一区| 亚洲av成人精品一二三区| 婷婷色麻豆天堂久久| 国产福利在线免费观看视频| 色94色欧美一区二区| 亚洲国产日韩一区二区| 日韩精品免费视频一区二区三区| 黑人巨大精品欧美一区二区蜜桃| 麻豆精品久久久久久蜜桃| 欧美日韩av久久| 又黄又粗又硬又大视频| 中国国产av一级| 国产人伦9x9x在线观看 | 国产日韩欧美亚洲二区| 韩国精品一区二区三区| 精品99又大又爽又粗少妇毛片| 18禁动态无遮挡网站| 中文字幕制服av| 欧美 日韩 精品 国产| 三级国产精品片| 一级a爱视频在线免费观看| 色婷婷久久久亚洲欧美| 日韩免费高清中文字幕av| 王馨瑶露胸无遮挡在线观看| 久久青草综合色| 黄色怎么调成土黄色| 黑人欧美特级aaaaaa片| av天堂久久9| 色哟哟·www| 色哟哟·www| 在线观看三级黄色| 亚洲美女视频黄频| 久久久精品国产亚洲av高清涩受| 啦啦啦在线观看免费高清www| 亚洲av免费高清在线观看| 国产亚洲av片在线观看秒播厂| 亚洲中文av在线| 2022亚洲国产成人精品| 色哟哟·www| xxx大片免费视频| 精品一区二区免费观看| 国产女主播在线喷水免费视频网站| 亚洲av欧美aⅴ国产| 精品福利永久在线观看| 一级,二级,三级黄色视频| 伊人亚洲综合成人网| 国产福利在线免费观看视频| 精品人妻偷拍中文字幕| 国产成人精品无人区| 纵有疾风起免费观看全集完整版| 你懂的网址亚洲精品在线观看| 国产一区二区激情短视频 | 在线观看免费高清a一片| 国产一区二区激情短视频 | 男女无遮挡免费网站观看| 国产色婷婷99| 国产成人午夜福利电影在线观看| 免费看不卡的av| 91精品三级在线观看| 伦理电影大哥的女人| 五月开心婷婷网| 色网站视频免费| 香蕉丝袜av| 男女免费视频国产| 制服丝袜香蕉在线| 日韩一本色道免费dvd| 男女啪啪激烈高潮av片| 18在线观看网站| 亚洲精品日韩在线中文字幕| 日韩av不卡免费在线播放| 国产黄色免费在线视频| 欧美日韩成人在线一区二区| 一区二区三区精品91| 国产精品偷伦视频观看了| 久久精品久久久久久噜噜老黄| 亚洲av成人精品一二三区| 亚洲伊人久久精品综合| 欧美日韩亚洲国产一区二区在线观看 | 久久人人爽人人片av| 亚洲精品第二区| 婷婷色av中文字幕| 欧美成人午夜免费资源| 天天躁狠狠躁夜夜躁狠狠躁| av视频免费观看在线观看| 91精品三级在线观看| 亚洲经典国产精华液单| 欧美xxⅹ黑人| 中文天堂在线官网| 久久99一区二区三区| 视频在线观看一区二区三区| 青草久久国产| 99热国产这里只有精品6| av片东京热男人的天堂| xxx大片免费视频| 午夜久久久在线观看| 青青草视频在线视频观看| 国产片特级美女逼逼视频| 天天躁狠狠躁夜夜躁狠狠躁| 老汉色av国产亚洲站长工具| 18在线观看网站| 欧美中文综合在线视频| 亚洲欧美清纯卡通| 午夜福利视频在线观看免费| 欧美日韩综合久久久久久| 亚洲伊人久久精品综合| 高清不卡的av网站| 久久人人爽人人片av| 深夜精品福利| 午夜av观看不卡| 黑人猛操日本美女一级片| 曰老女人黄片| 午夜免费观看性视频| 天堂中文最新版在线下载| 成年女人毛片免费观看观看9 | 一区二区三区精品91| 国产激情久久老熟女| 97人妻天天添夜夜摸| 亚洲三级黄色毛片| 久久99一区二区三区| 成人国产av品久久久| 亚洲欧美精品综合一区二区三区 | 美女大奶头黄色视频| 丝袜喷水一区| 国产精品不卡视频一区二区| 亚洲av电影在线进入| 男女啪啪激烈高潮av片| 最新中文字幕久久久久| 国产精品欧美亚洲77777| av在线播放精品| 丝袜脚勾引网站| 久久久久久免费高清国产稀缺| 新久久久久国产一级毛片| 国产精品麻豆人妻色哟哟久久| 日韩精品免费视频一区二区三区| 日本黄色日本黄色录像| 男女免费视频国产| 婷婷色麻豆天堂久久| 精品一区二区三区四区五区乱码 | 免费观看性生交大片5| 国产1区2区3区精品| 91成人精品电影| 亚洲欧美一区二区三区国产| 中文字幕精品免费在线观看视频| 亚洲成人一二三区av| 日韩视频在线欧美| 岛国毛片在线播放| 纵有疾风起免费观看全集完整版| 欧美日韩视频精品一区| 亚洲精品乱久久久久久| 我的亚洲天堂| 免费在线观看视频国产中文字幕亚洲 | av福利片在线| 丰满乱子伦码专区| videossex国产| 性色av一级| 午夜福利乱码中文字幕| 十八禁网站网址无遮挡| 纵有疾风起免费观看全集完整版| 搡女人真爽免费视频火全软件| 一级片'在线观看视频| 好男人视频免费观看在线| 国产亚洲一区二区精品| 国产一区有黄有色的免费视频| 一本一本久久a久久精品综合妖精 国产伦在线观看视频一区 | 亚洲av中文av极速乱| 美女国产视频在线观看| 亚洲,一卡二卡三卡| 国产片特级美女逼逼视频| 99久久人妻综合| 男女无遮挡免费网站观看| 久久久国产精品麻豆| freevideosex欧美| 国产精品秋霞免费鲁丝片| 中文字幕最新亚洲高清| 美国免费a级毛片| 国产一级毛片在线| 午夜福利一区二区在线看| 亚洲国产精品一区二区三区在线| 看非洲黑人一级黄片| www.熟女人妻精品国产| 国产精品嫩草影院av在线观看| av线在线观看网站| 国产探花极品一区二区| 婷婷色麻豆天堂久久| 久久久国产一区二区| 亚洲国产最新在线播放| 国产亚洲一区二区精品| 亚洲精品久久成人aⅴ小说| 女人久久www免费人成看片| 欧美精品一区二区免费开放| 日韩av免费高清视频| 最近手机中文字幕大全| 久久久久久人妻| 十八禁网站网址无遮挡| av在线观看视频网站免费| 啦啦啦在线观看免费高清www| 王馨瑶露胸无遮挡在线观看| 伊人久久大香线蕉亚洲五| 久久av网站| 亚洲一区二区三区欧美精品| 欧美中文综合在线视频| 在线 av 中文字幕| 国产精品久久久av美女十八| 国产高清不卡午夜福利| 日韩精品有码人妻一区| 黄色怎么调成土黄色| 亚洲欧美日韩另类电影网站| 波多野结衣av一区二区av| 美女高潮到喷水免费观看| 日本色播在线视频| 成人18禁高潮啪啪吃奶动态图| 99久国产av精品国产电影| 国产日韩欧美亚洲二区| 亚洲av电影在线进入| av在线观看视频网站免费| 国产精品一区二区在线不卡| 九色亚洲精品在线播放| 午夜福利乱码中文字幕| 国产成人精品久久久久久| 日日撸夜夜添| 热re99久久国产66热| 国产深夜福利视频在线观看| 性高湖久久久久久久久免费观看| 99香蕉大伊视频| 日日摸夜夜添夜夜爱| 晚上一个人看的免费电影| 亚洲,欧美,日韩| 中文欧美无线码| 综合色丁香网| 婷婷色av中文字幕| 91精品三级在线观看| 日本色播在线视频| 亚洲色图综合在线观看| 另类精品久久| 欧美+日韩+精品| 亚洲色图 男人天堂 中文字幕| 日本av手机在线免费观看| 如何舔出高潮| 捣出白浆h1v1| 日本午夜av视频| 看免费av毛片| 欧美亚洲日本最大视频资源| 国产探花极品一区二区| 五月伊人婷婷丁香| 国产精品久久久久久av不卡| 欧美成人精品欧美一级黄| 性高湖久久久久久久久免费观看| 免费播放大片免费观看视频在线观看| 国产av国产精品国产| 成年av动漫网址| 波野结衣二区三区在线| 精品一区二区三区四区五区乱码 | 国产精品99久久99久久久不卡 | 亚洲美女搞黄在线观看| 国产av精品麻豆| av又黄又爽大尺度在线免费看| 日韩三级伦理在线观看| 久久久国产一区二区| 建设人人有责人人尽责人人享有的| 免费女性裸体啪啪无遮挡网站| 午夜激情久久久久久久| 亚洲五月色婷婷综合| 免费高清在线观看日韩| 久久午夜综合久久蜜桃| av在线app专区| 午夜日本视频在线| 美国免费a级毛片| 热99久久久久精品小说推荐| 久久这里只有精品19| 韩国高清视频一区二区三区| 欧美最新免费一区二区三区| www.熟女人妻精品国产| 新久久久久国产一级毛片| 欧美亚洲 丝袜 人妻 在线| 亚洲精品国产av成人精品| 国产精品二区激情视频| 美女国产视频在线观看| 老汉色∧v一级毛片| 丝袜美腿诱惑在线| 国产精品一区二区在线不卡| 精品亚洲成a人片在线观看| 黄片无遮挡物在线观看| 最近手机中文字幕大全| 蜜桃在线观看..| 久久这里只有精品19| 亚洲欧美精品综合一区二区三区 | 少妇人妻久久综合中文| 黄网站色视频无遮挡免费观看| 国产av码专区亚洲av| 日本爱情动作片www.在线观看| 日韩精品免费视频一区二区三区| 日本午夜av视频| 老司机亚洲免费影院| 国产欧美亚洲国产| 精品国产乱码久久久久久小说| 亚洲欧美一区二区三区久久| 国产日韩欧美视频二区| 国产在线视频一区二区| 搡女人真爽免费视频火全软件| 亚洲精品日本国产第一区| 国产成人一区二区在线| 中文字幕人妻熟女乱码| 国产精品久久久久久av不卡| 国产亚洲精品第一综合不卡| 免费观看av网站的网址| 欧美日韩亚洲国产一区二区在线观看 | kizo精华| 日本vs欧美在线观看视频| 女性被躁到高潮视频| 欧美激情 高清一区二区三区| 欧美日韩精品成人综合77777| 9191精品国产免费久久| 青春草视频在线免费观看| 国产 一区精品| 亚洲国产最新在线播放| 国产av一区二区精品久久| 精品国产乱码久久久久久小说| 亚洲av免费高清在线观看| 国产伦理片在线播放av一区| 菩萨蛮人人尽说江南好唐韦庄| 日本欧美国产在线视频| 免费看不卡的av| 亚洲欧美成人综合另类久久久| 99久国产av精品国产电影| 国产不卡av网站在线观看| av福利片在线| 国产精品熟女久久久久浪| 国产成人午夜福利电影在线观看| 日本午夜av视频| 亚洲av免费高清在线观看| 亚洲久久久国产精品| 成年女人毛片免费观看观看9 | 中文字幕最新亚洲高清| 国产爽快片一区二区三区| 伊人亚洲综合成人网| 少妇猛男粗大的猛烈进出视频| 秋霞伦理黄片| 亚洲色图 男人天堂 中文字幕| 亚洲欧美一区二区三区久久| 午夜福利乱码中文字幕| 久久精品国产自在天天线| 老司机影院成人| 国产成人一区二区在线| 日韩中文字幕视频在线看片| 如何舔出高潮| 日韩av在线免费看完整版不卡| 亚洲国产欧美网| 热99久久久久精品小说推荐| 成人国产麻豆网| 国产成人aa在线观看| 国产成人精品福利久久| 国产男人的电影天堂91| 韩国高清视频一区二区三区| 伦理电影大哥的女人| 日本猛色少妇xxxxx猛交久久| 中文字幕制服av| 亚洲一区二区三区欧美精品| 亚洲精品国产av成人精品| a级片在线免费高清观看视频| 视频区图区小说| 欧美日韩精品网址| 黄色配什么色好看| 日韩av免费高清视频| 晚上一个人看的免费电影| 国产女主播在线喷水免费视频网站| 校园人妻丝袜中文字幕| 成年动漫av网址| 久久久精品国产亚洲av高清涩受| 亚洲三区欧美一区| 国产成人精品无人区| 国产成人免费无遮挡视频| 赤兔流量卡办理| 日韩中文字幕视频在线看片| 亚洲精品国产av蜜桃| freevideosex欧美| 国产精品嫩草影院av在线观看| 看十八女毛片水多多多| 国产高清国产精品国产三级| 在线观看一区二区三区激情| 久久精品久久久久久噜噜老黄| 三级国产精品片| 天天操日日干夜夜撸| 人妻人人澡人人爽人人| 精品国产一区二区三区四区第35| 五月伊人婷婷丁香| 亚洲欧美精品综合一区二区三区 | 日韩av不卡免费在线播放| av电影中文网址| 国产白丝娇喘喷水9色精品| 国产一区二区 视频在线| 男人添女人高潮全过程视频| 精品久久久久久电影网| 黄色 视频免费看| 亚洲av中文av极速乱| 老司机影院成人| 香蕉丝袜av| 精品卡一卡二卡四卡免费| 久久久久精品人妻al黑| 欧美另类一区| videos熟女内射| 久久精品亚洲av国产电影网| 老司机亚洲免费影院| 国产高清不卡午夜福利| 日韩成人av中文字幕在线观看| 午夜福利乱码中文字幕| 亚洲av福利一区| 亚洲国产毛片av蜜桃av| 色婷婷av一区二区三区视频| 久久人人97超碰香蕉20202| 日本色播在线视频| 久久精品久久久久久久性| 国产黄色免费在线视频| a 毛片基地| 国产女主播在线喷水免费视频网站| av电影中文网址| 色网站视频免费| 国产1区2区3区精品| 亚洲成色77777| 卡戴珊不雅视频在线播放| 伦精品一区二区三区| 在线观看国产h片| 国产成人午夜福利电影在线观看| 亚洲欧洲日产国产| 国产又爽黄色视频| 搡女人真爽免费视频火全软件| 永久网站在线| 国产黄频视频在线观看| 午夜免费男女啪啪视频观看| 婷婷色综合大香蕉| 热99国产精品久久久久久7| 欧美最新免费一区二区三区| av一本久久久久| 亚洲少妇的诱惑av| 精品酒店卫生间| 色视频在线一区二区三区| 午夜免费观看性视频| 大码成人一级视频| 国产免费又黄又爽又色| 日韩中文字幕视频在线看片| 男女午夜视频在线观看| 婷婷色综合大香蕉| 伊人久久大香线蕉亚洲五| 少妇人妻 视频| 十八禁高潮呻吟视频| 亚洲综合色惰| 国产免费又黄又爽又色| 成人18禁高潮啪啪吃奶动态图| 国产又色又爽无遮挡免| 丰满迷人的少妇在线观看| 岛国毛片在线播放| 午夜福利视频精品| 熟女电影av网| 丝袜美足系列| 精品国产乱码久久久久久男人| 亚洲精品日本国产第一区| 亚洲激情五月婷婷啪啪| 成人二区视频| 一区在线观看完整版| 亚洲图色成人| 777米奇影视久久| 人妻 亚洲 视频|