Injury Severity Of Traffic Accidents Research Articles

Accident Severity Prediction using Machine Learning

Traffic accidents are among the most critical issues facing the world as they cause many deaths, injuries, and fatalities as well as economic losses every year. Accurate models to predict the traffic accident severity is a critical task for transportation systems. This investigation effort establishes models to select a set of influential factors and to build up a model for classifying the severity of injuries. These models are formulated by various machine learning techniques. Supervised machine learning algorithms and unsupervised machine learning algorithms are implemented on traffic accident data. The major objective is to discover the correlation between different types of the traffic accidents with the type of the injuries. The findings of this study indicate that the unsupervised learning techniques can be a promising tool for predicting the injury severity of traffic accidents.

An analytic framework using deep learning for prediction of traffic accident injury severity based on contributing factors

Vulnerable road users (VRUs) are exposed to the highest risk in the road traffic environment. Analyzing contributing factors that affect injury severity facilitates injury severity prediction and further application in developing countermeasures to guarantee VRUs safety. Recently, machine learning approaches have been introduced, in which analyses tend to be one-sided and may ignore important information. To solve this problem, this paper proposes a comprehensive analytic framework that employs a deep learning model referred to as the stacked sparse autoencoder (SSAE) to predict the injury severity of traffic accidents based on contributing factors. The essential idea of the method is to integrate various analyses into an analytical framework that performs corresponding data processing and analysis by different machine learning approaches. In the proposed method, first, we utilize a machine learning approach (i.e., Catboost) to analyze the importance and dependence of the contributing factors to injury severity and remove low correlation factors; second, according to the geographical information, we classify the data into different classes by utilizing a machine learning approach (i.e., k-means clustering); third, by employing high correlation factors, we employ an SSAE-based deep learning model to perform injury severity prediction in each data class. By experiments with a real-world traffic accident dataset, we demonstrated the effectiveness and applicability of the framework. Specifically, (1) the importance and dependence of contributing factors were obtained by CatBoost and the Shapley value, and (2) the SSAE-based deep learning model achieved the best performance compared to other baseline models. The proposed analytic framework can also be utilized for other accident data for severity or other risk indicator analyses involving VRUs safety.

Pedestrians’ Injury Severity in Traffic Accidents in Spain: A Pedestrian Actions Approach

Road traffic accidents are currently between the seventh and tenth leading cause of death in the world, with approximately 1.35 million people killed per year. Despite extensive efforts by governments, according to the World Health Organization, road accidents still cause far too many deaths, especially among pedestrians, cyclists and two-wheel motor vehicle riders, who together account for almost 50% of road traffic fatalities. In particular, Spain had 410,974 traffic accidents between 2016 and 2019, involving 722,516 vehicles and 61,177 pedestrians with varying degrees of injury. This study uses the Bayesian network method to understand how the pedestrians’ responsibility and actions at the time of the traffic accident affect the injury suffered by said pedestrian, also considering the variables of the road infrastructure and vehicles at the accident site. The results confirm that the variables linked to the unsafe behavior of pedestrians, and their responsibility in traffic accidents, increase the risk of suffering serious or fatal injuries during an accident; for example, if a pedestrian is distracted this increases his/her probability of suffering a severe injury (27.86%) with respect to not being distracted (20.73%). Conditions related to traffic in high-speed areas, areas with no or poor lighting, and areas lacking sidewalks, also record increases in pedestrian injury, as is the case in the age group of pedestrians over 60 years of age.

Identification of Hotspot Segments with a Risk of Heavy-Vehicle Accidents Based on Spatial Analysis at Controlled-Access Highway

Significant risk factors that influence the occurrence of heavy vehicle accidents have been explored in numerous studies in order to lower injury severity in traffic accidents. It is imperative to explore road sections with a high risk of heavy vehicle accident occurrence by considering the significant consequences of such accidents for road users, despite the low number of heavy vehicles in traffic flow. To address this, this study proposes a method to predict clustering hotspots for heavy vehicle accidents on the basis of three different criteria, namely, heavy vehicle accident cases, the number of heavy vehicles involved, and accident severity index values. Moran’s I spatial autocorrelation was employed to identify the clustering for each criterion, and the Getis–Ord Gi* statistic was applied to estimate the likelihood of risk along the network. This study considers the features of hotspot points at significance levels from 0.10 to 0.01 with a 1355 m buffer radius to create segments for each criterion. The three criteria for hotspots were considered within the overlapped buffer zone. A total of 22 heavy vehicle risk segments (HVRSs) were identified and then ranked by crash rate. Overall, this study demonstrates the application of different criteria to identify accident hotspots involving a specific vehicle type, which could help in prioritizing segments with a high risk of heavy vehicle accidents, as well as providing information for HVRSs for the purpose of developing appropriate countermeasures for the identified accident hotspots.

СОВЕРШЕНСТВОВАНИЕ ЗАКОНОДАТЕЛЬСТВА, УСТАНАВЛИВАЮЩЕГО ОТВЕТСТВЕННОСТЬ ЗА НАРУШЕНИЕ ПОРЯДКА ПРЕДОСТАВЛЕНИЯ ГОСУДАРСТВЕННЫХ УСЛУГ В СФЕРЕ ОБЕСПЕЧЕНИЯ БЕЗОПАСНОСТИ ДОРОЖНОГО ДВИЖЕНИЯ

Study of the tasks and functions of police in the sphere of traffic safety is dedicated to many scientific researches due to the fact that the significance of the topic has not lost its relevance, the evidence of the disappointing official statistics about the dead and received various degrees of injury severity in traffic accidents and also due to the fact that the quantity of transport in Russia is constantly increasing, and the problems of transport security are not solved. At the same time, research related to the implementation by the police of the functions of providing public services in this area is of particular importance, because despite the fact that this area of police activity is not the main one, and is not provided with state-power enforcement, it nevertheless plays a significant role in ensuring public safety in the field of traffic. Indirectly, the police, through the function of providing public services, by issuing permits for the transport of dangerous goods, issuing permits for the right to drive, etc., minimizes and reduces the threat of harm to life and health, property, and citizens involved in road traffic. In the article, the author sets a task to investigate the legislation that establishes liability for violation of the procedure for providing public services in the field of road safety, to suggest ways to improve it. The author defines public services in the field of road safety and defines their essence. The author analyzes Federal and departmental legislation, identifies the features of bringing to justice police officers for improper provision of public services. The article draws conclusions about the existence of certain shortcomings in the legislation in this area and suggests ways to eliminate them.

Traffic Accidents Classification and Injury Severity Prediction

Traffic accidents are one of the most life-threatening dangers to human being. Deaths and injuries due to traffic accidents have a great impact on society. Traffic accidents information and data provided by public can be useful to classify these accidents according to their type and severity, and consequently try to build predictive model. Detecting and identifying injury severity in traffic accidents in real time is primordial for speeding post-accidents protocols as well as developing general road safety policies. In this project we are using Logistic Regression algorithm to classify accident data. The data to be analysed is collected from various sources, is both structured and unstructured and has several attributes. In this project we are going to detect and analyse data together to generate decision trees that give insights on previous accidents.

Relationship between age and injury severity in traffic accidents involving elderly pedestrians.

ObjectiveThis study aimed to examine whether injury severity differs with respect to age among elderly pedestrians involved in traffic accidents and identify factors affecting injury severity.MethodsUsing emergency department-based injury in-depth surveillance data, we analyzed the data of patients aged ≥60 years who were victims of pedestrian traffic accidents during 2011 to 2016. The pedestrians’ ages were divided into 5-year age strata beginning at 60 years. In a multivariate analysis, injury severity was classified as severe to critical or mild to moderate.ResultsThe analysis included 10,449 patients. All age groups had a female predominance, and accidents most frequently occurred during the early morning. Multivariate analyses revealed that compared to the 60 to 64 years group, the odds ratios for incurring a severe injury were 1.18 (95% confidence interval [CI], 1.02 to 1.37) for the 65 to 69 years group, 1.42 (95% CI, 1.23 to 1.64) for the 70 to 74 years group, 1.70 (95% CI, 1.45 to 1.98) for the 75 to 79 years group, and 1.83 (95% CI, 1.56 to 2.15) for the ≥80 years group.ConclusionIn this study of emergency department-based data, we found that injury severity increased with age among elderly victims of traffic accidents. Furthermore, injury severity varied with respect to sex, time and location of the accident, and type of vehicle involved. Therefore, measures intended to reduce and prevent traffic accidents involving elderly pedestrians should consider these findings.

Effect of Age on Severity of Injuries in Traffic Accidents Involving Elderly Drivers

Background: The number of elderly drivers involves in traffic accidents is increasing with the aging of the population. This study aimed to investigate the relationship between the driver's age and injury severity in traffic accidents caused by elderly drivers. Methods: Data from the Emergency Department-based Injury In-depth Surveillance (EDIIS) from 2011-2016 was used to analyze traffic accidents by drivers aged 60 and older. Patients were classified into non-severe and severe injury group. The driver's age was subdivided by 5-year interval to compare the difference in age groups. Multivariate logistic regression was used to identify the factors related to the severe injury. Results: Among a total of 262,361 subjects, 6,427 patients were included, of which 890 (13.9%) were severely injured. There were more male in all age groups. The accidents occurred most in the daytime and general roads. In multivariate analysis, drivers aged 75-79 had 1.39 odds ratio (OR) of severe injury compared to drivers aged 60-64 (95% confidence interval [CI]: 1.06-1.84). Male (OR: 1.71, 95% CI: 1.40-2.09), alcohol intake (OR: 2.02, 95% CI: 1.39-2.94), and non-use of seatbelt (OR: 2.06, 95% CI: 1.71-2.48) were associated with severe injury, as well as the counterpart and road class. Conclusion: Among elderly drivers, there were significant differences in characteristics of traffic accidents according to age groups. The risk of severe injury increased in drivers aged over 75 years comparing to relatively young elderly drivers. These results provide the practical adjustment in determining the age standard when establishing traffic safety policies.

Severity Prediction of Traffic Accidents with Recurrent Neural Networks

In this paper, a deep learning model using a Recurrent Neural Network (RNN) was developed and employed to predict the injury severity of traffic accidents based on 1130 accident records that have occurred on the North-South Expressway (NSE), Malaysia over a six-year period from 2009 to 2015. Compared to traditional Neural Networks (NNs), the RNN method is more effective for sequential data, and is expected to capture temporal correlations among the traffic accident records. Several network architectures and configurations were tested through a systematic grid search to determine an optimal network for predicting the injury severity of traffic accidents. The selected network architecture comprised of a Long-Short Term Memory (LSTM) layer, two fully-connected (dense) layers and a Softmax layer. Next, to avoid over-fitting, the dropout technique with a probability of 0.3 was applied. Further, the network was trained with a Stochastic Gradient Descent (SGD) algorithm (learning rate = 0.01) in the Tensorflow framework. A sensitivity analysis of the RNN model was further conducted to determine these factors’ impact on injury severity outcomes. Also, the proposed RNN model was compared with Multilayer Perceptron (MLP) and Bayesian Logistic Regression (BLR) models to understand its advantages and limitations. The results of the comparative analyses showed that the RNN model outperformed the MLP and BLR models. The validation accuracy of the RNN model was 71.77%, whereas the MLP and BLR models achieved 65.48% and 58.30% respectively. The findings of this study indicate that the RNN model, in deep learning frameworks, can be a promising tool for predicting the injury severity of traffic accidents.

Multinomial Logit Framework to Evaluate the Impact of Seating Position on Senior Occupant Injury Severity in Traffic Accidents

Multinomial Logit Framework to Evaluate the Impact of Seating Position on Senior Occupant Injury Severity in Traffic Accidents

Severity Prediction of Traffic Accident Using an Artificial Neural Network

AbstractIn this paper, an artificial neural network (ANN) was used to predict the injury severity of traffic accidents based on 5973 traffic accident records occurred in Abu Dhabi over a 6‐year period (from 2008 to 2013). For each accident record, 48 different attributes had been collected at the time of the accident. After data preprocessing, the data were reduced to 16 attributes and four injury severity classes. In this study, WEKA (Waikato Environment for Knowledge Analysis) data‐mining software was used to build the ANN classifier. The traffic accident data were used to build two classifiers in two different ways. The whole data set were used for training and validating the first classifier (training set), while 90% of the data were used for training the second classifier and the remaining 10% were used for testing it (testing set). The experimental results revealed that the developed ANN classifiers can predict accident severity with reasonable accuracy. The overall model prediction performance for the training and testing data were 81.6% and 74.6%, respectively. To improve the prediction accuracy of the ANN classifier, traffic accident data were split into three clusters using a k‐means algorithm. The results after clustering revealed significant improvement in the prediction accuracy of the ANN classifier, especially for the training dataset. In this work, and in order to validate the performance of the ANN model, an ordered probit model was also used as a comparative benchmark. The dependent variable (i.e. degree of injury) was transformed from ordinal to numerical (1, 2, 3, 4) for (minor, moderate, sever, death). The R tool was used to perform an ordered probit. For each accident, the ordered probit model showed how likely this accident would result in each class (minor, moderate, severe, death). The accuracy of 59.5% obtained from the ordered probit model was clearly less than the ANN accuracy value of 74.6%. Copyright © 2016 John Wiley & Sons, Ltd.

Bayes classifiers for imbalanced traffic accidents datasets

Traffic accidents data sets are usually imbalanced, where the number of instances classified under the killed or severe injuries class (minority) is much lower than those classified under the slight injuries class (majority). This, however, supposes a challenging problem for classification algorithms and may cause obtaining a model that well cover the slight injuries instances whereas the killed or severe injuries instances are misclassified frequently. Based on traffic accidents data collected on urban and suburban roads in Jordan for three years (2009–2011); three different data balancing techniques were used: under-sampling which removes some instances of the majority class, oversampling which creates new instances of the minority class and a mix technique that combines both. In addition, different Bayes classifiers were compared for the different imbalanced and balanced data sets: Averaged One-Dependence Estimators, Weightily Average One-Dependence Estimators, and Bayesian networks in order to identify factors that affect the severity of an accident. The results indicated that using the balanced data sets, especially those created using oversampling techniques, with Bayesian networks improved classifying a traffic accident according to its severity and reduced the misclassification of killed and severe injuries instances. On the other hand, the following variables were found to contribute to the occurrence of a killed causality or a severe injury in a traffic accident: number of vehicles involved, accident pattern, number of directions, accident type, lighting, surface condition, and speed limit. This work, to the knowledge of the authors, is the first that aims at analyzing historical data records for traffic accidents occurring in Jordan and the first to apply balancing techniques to analyze injury severity of traffic accidents.

Injury severity models for motor vehicle accidents: a review

Modelling of traffic accidents injury severity is a complex task. In the last few years the number and variety of studies that analyse injury severity of traffic accidents have increased considerably. In this paper 19 modelling techniques used to model injury severity of traffic accidents where at least a four-wheeled vehicle is involved have been analysed. The analysis and the comparison between models was performed based on seven criteria (modelling technique, number of records, number of variables, area type, features, injury level and model fit). In general, it is not possible to recommend a method that could be identified as the best one. Each modelling technique has its own limitations and characteristics, awareness of which will help analysts to decide the best method to be used in each particular modelling problem. However, some general conclusions can be established: in most cases the results of models' fits are found to be satisfactory, though not excellent; in the case of data mining models, accuracy improves with balanced datasets; and no correlation was found to exist between the number of accident records and the number of analysed variables.

Analysis of driver injury severity in truck-involved accidents using a non-parametric classification tree model

To explore the factors contributing to driver injury severity in traffic accidents, parametric regression models, such as multinomial logit models (MNLs) or ordered probabilistic regression models, have been commonly applied for many years. However, these parametric models have their own model assumptions and pre-defined underlying relationships between dependent and independent variables. If these assumptions are violated, the models can lead to erroneous estimation of the injury likelihood. This study collects the 2005–2006 truck-involved accident data from national freeways in Taiwan and develops a non-parametric Classification and Regression Tree (CART) model to establish the empirical relationship between injury severity outcomes and driver/vehicle characteristics, highway geometric variables, environmental characteristics, and accident variables. The results show that drinking-driving, seatbelt use, vehicle type, collision type, contributing circumstance and driver/vehicle action, number of vehicles involved in the accident and accident location were the key determinants of injury severity outcomes for truck accidents.

Risk Factors for the Injury Severity of Fatigue-Related Traffic Accidents

Traffic accidents caused by drivers’ fatigue carry less than one percent of the whole accidents in HLJ Province during the years 2006 to 2008. However, more than forty percent of such accidents accompanied fatalities. Drivers’ fatigue is usually hard to be identified and there are no valid measures that could make real-time detection for it. Accordingly, variables such as drivers’ characteristics, time of accident and whether using seat belt are considered to have close association with the injury severity in fatigue-related traffic accidents. This research focuses on analyzing injury severities of traffic accidents caused by drivers’ fatigue, utilizing stepwise logistic regression method. Potential risk factors such as human, environment, road, and so on, were examined. Driving year, road pavement type, road grade and alignment, terrain, time and type of the accident, streetlight condition, vehicle type, speed limit, the number of vehicles involved, and whether using seat belt are significant factors impacting the injury severity. Identifying the high risk factors influencing the injury severity of fatigue-related accidents helps prevent the occurrence of drivers’ fatigue and improve road safety conditions.

Injury Rates for Older and Younger Belted Drivers in Traffic Accidents

This paper describes the correlation of a person's age to the risk of injury occurrence and the corresponding injury severity in traffic accidents. A representative sample of belted drivers was analyzed by using data from the German In-Depth-Accident Study (GIDAS) to investigate the influence of age on injury severity and special injuries to different body regions. The study focused on two age groups: 17-30 year old (younger drivers) and older drivers 50 year old and older (50+). The injury risk was described as a function of delta-v and injury risk curves based on Abbreviated Injury Scale (AIS). Furthermore, individual parameters like age and body mass index (BMI) as well as age and mass of the vehicle were considered. The statistical analysis was carried out using descriptive and multivariate statistics. This paper presents an overview of injury patterns of belted drivers and the probability of these drivers being injured in different accident scenarios. The age-related probability for special injury patterns to different body regions was shown for younger versus older drivers. A risk score to predict the injury severity expressed in relation with AIS for different body regions was developed from the resulting statistical analysis. Language: en

A method for simplifying the analysis of traffic accidents injury severity on two-lane highways using Bayesian networks

Introduction This study describes a method for reducing the number of variables frequently considered in modeling the severity of traffic accidents. The method's efficiency is assessed by constructing Bayesian networks (BN). Method It is based on a two stage selection process. Several variable selection algorithms, commonly used in data mining, are applied in order to select subsets of variables. BNs are built using the selected subsets and their performance is compared with the original BN (with all the variables) using five indicators. The BNs that improve the indicators’ values are further analyzed for identifying the most significant variables (accident type, age, atmospheric factors, gender, lighting, number of injured, and occupant involved). A new BN is built using these variables, where the results of the indicators indicate, in most of the cases, a statistically significant improvement with respect to the original BN. Conclusions It is possible to reduce the number of variables used to model traffic accidents injury severity through BNs without reducing the performance of the model. Impact on Industry The study provides the safety analysts a methodology that could be used to minimize the number of variables used in order to determine efficiently the injury severity of traffic accidents without reducing the performance of the model.

Analysis of traffic accident injury severity on Spanish rural highways using Bayesian networks

Several different factors contribute to injury severity in traffic accidents, such as driver characteristics, highway characteristics, vehicle characteristics, accidents characteristics, and atmospheric factors. This paper shows the possibility of using Bayesian Networks (BNs) to classify traffic accidents according to their injury severity. BNs are capable of making predictions without the need for pre assumptions and are used to make graphic representations of complex systems with interrelated components. This paper presents an analysis of 1536 accidents on rural highways in Spain, where 18 variables representing the aforementioned contributing factors were used to build 3 different BNs that classified the severity of accidents into slightly injured and killed or severely injured. The variables that best identify the factors that are associated with a killed or seriously injured accident (accident type, driver age, lighting and number of injuries) were identified by inference.

Forensic pathological evaluation of injury severity and fatal outcome in traffic accidents: five illustrative autopsy cases of clinically unexpected death

In forensic casework, investigation of injury severity in traffic accidents is important for evaluating the mortality, occasionally in terms of the adequacy of clinical management. The present study evaluated 5 cases of clinically unexpected delayed collapse followed by death using the abbreviated injury scale (AIS), injury severity score (ISS), and a clinical trauma care method (trauma and injury severity score, TRISS). In these cases, major injury (AIS = 3-5) was found in the head, chest and/or abdomen at autopsy, and ISS was estimated to be 11-45 (serious to critical but not incompatible with life). By the TRISS method, the probability of survival (P (s)) was estimated to be >0.5 for all cases (0.60-0.99), suggesting that these were preventable deaths. However, the present cases showed several common features: (a) fatality due to closed injury/-ies to the thoracic and/or abdominal viscera, (b) alert and poor symptoms/clinical signs, and (c) poor positive findings in diagnostic imaging at early times after injury, and (d) complications of other evident injuries, suggesting difficulties in the clinical diagnosis of potentially fatal injuries, but (e) possibly predictable fatal injury when typical patterns of traffic accident injury were considered.

Analysis of traffic injury severity: An application of non-parametric classification tree techniques

Statistical regression models, such as logit or ordered probit/logit models, have been widely employed to analyze injury severity of traffic accidents. However, most regression models have their own model assumptions and pre-defined underlying relationships between dependent and independent variables. If these assumptions are violated, the model could lead to erroneous estimations of injury likelihood. The classification and regression tree (CART), one of the most widely applied data mining techniques, has been commonly employed in business administration, industry, and engineering. CART does not require any pre-defined underlying relationship between target (dependent) variable and predictors (independent variables) and has been shown to be a powerful tool, particularly for dealing with prediction and classification problems. This study uses the 2001 accident data for Taipei, Taiwan. A CART model was developed to establish the relationship between injury severity and driver/vehicle characteristics, highway/environmental variables and accident variables. The results indicate that the most important variable associated with crash severity is the vehicle type. Pedestrians, motorcycle and bicycle riders are identified to have higher risks of being injured than other types of vehicle drivers in traffic accidents.