Severity Of Road Traffic Accidents Research Articles

Analysis of machine learning models for traffic accidents severity classification

In the modern world, traffic accidents frequently result in fatalities and serious injuries. The ability of machine learning to foretell the severity of road traffic accidents has shown great promise. The classification of traffic accidents has shown to be a good application for algorithms like random forest. In this paper, performance on a specific dataset has been evaluated using random forest and other models. The dataset used for the analysis came from a publicly accessible source and contained information on several variables like the type of road, the time of day, and the weather. In order to analyze the severity of accidents, a number of algorithms were applied to the dataset, including decision tree, random forest classifier, and logistic regression algorithm. Each model was evaluated on parameters such as model accuracy, precision and recall of the model, and F1 score. The random forest classifier outperformed the other models, achieving an accuracy of 98.48%. The study concludes that machine learning algorithms can accurately predict the severity of road traffic accidents, which could help to reduce the number of accidents and fatalities on the road.

Enhancing prediction and analysis of UK road traffic accident severity using AI: Integration of machine learning, econometric techniques, and time series forecasting in public health research

This research project explored into the intricacies of road traffic accidents severity in the UK, employing a potent combination of machine learning algorithms, econometric techniques, and traditional statistical methods to analyse longitudinal historical data. Our robust analysis framework includes descriptive, inferential, bivariate, multivariate methodologies, correlation analysis: Pearson's and Spearman's Rank Correlation Coefficient, multiple logistic regression models, Multicollinearity Assessment, and Model Validation. In addressing heteroscedasticity or autocorrelation in error terms, we've advanced the precision and reliability of our regression analyses using the Generalized Method of Moments (GMM). Additionally, our application of the Vector Autoregressive (VAR) model and the Autoregressive Integrated Moving Average (ARIMA) models have enabled accurate time series forecasting. With this approach, we've achieved superior predictive accuracy and marked by a Mean Absolute Scaled Error (MASE) of 0.800 and a Mean Error (ME) of -73.80 compared to a naive forecast. The project further extends its machine learning application by creating a random forest classifier model with a precision of 73%, a recall of 78%, and an F1-score of 73%. Building on this, we employed the H2O AutoML process to optimize our model selection, resulting in an XGBoost model that exhibits exceptional predictive power as evidenced by an RMSE of 0.1761205782994506 and MAE of 0.0874235576229789. Factor Analysis was leveraged to identify underlying variables or factors that explain the pattern of correlations within a set of observed variables. Scoring history, a tool to observe the model's performance throughout the training process was incorporated to ensure the highest possible performance of our machine learning models. We also incorporated Explainable AI (XAI) techniques, utilizing the SHAP (Shapley Additive Explanations) model to comprehend the contributing factors to accident severity. Features such as Driver_Home_Area_Type, Longitude, Driver_IMD_Decile, Road_Type, Casualty_Home_Area_Type, and Casualty_IMD_Decile were identified as significant influencers. Our research contributes to the nuanced understanding of traffic accident severity and demonstrates the potential of advanced statistical, econometric, machine learning techniques in informing evidence based interventions and policies for enhancing road safety.

ANALYSIS OF FACTORS AFFECTING DRIVING IN THE DARK TIME OF DAY

An analysis of factors affecting road traffic safety from the point of view of driving a car in the dark was carried out, in particular, the technical condition of the car, tinting of the windows, lighting of the streets (roads), speed of movement, driver fatigue. It has been established that a significant number of factors require significantly greater concentration and esponsibility from drivers. Driving a car in the dark requires special knowledge, skills and attention. The technical condition of vehicles becomes a number of priority problems. Vehicle malfunctions that are not detected in time or not eliminated increase the likelihood of a Road Traffic Accident. The cause of a Road Traffic Accident, especially in the dark period of the day, may be insufficient visibility of the road situation by the driver, which is caused by the violation of the requirements for tinting the glass in the car. The driver will not be able to fully assess everything that happens on the road at night both in conditions of insufficient visibility and in the presence of bad weather conditions. At the same time, proper street lighting can help reduce the risk of road traffic accidents by improving pedestrian visibility, reducing the likelihood of accidents and providing safer road conditions for both drivers and pedestrians. Speeding and incorrect selection of the speed regime in specific traffic conditions is one of the main factors affecting both the number and severity of Road Traffic Accidents. Another reason for the occurrence of a Road Transport Accident may be the driver’s fatigue, which reduces his concentration and ability to drive safely.

An Automated Approach for Predicting Road Traffic Accident Severity Using Transformer Learning and Explainable AI Technique

An Automated Approach for Predicting Road Traffic Accident Severity Using Transformer Learning and Explainable AI Technique

An analysis of causative factors for road accidents using partition around medoids and hierarchical clustering techniques

AbstractInsufficient progress in the development of national highways and state highways, coupled with a lack of public awareness regarding road safety, has resulted in prevalent traffic congestion and a high rate of accidents. Understanding the dominant and contributing factors that may influence road traffic accident severity is essential. This study identified the primary causes and the most significant target‐specific causative factors for road accident severity. A modified partitioning around medoids model determined the dominant road accident features. These clustering algorithms will extract hidden information from the road accident data and generate new features for our implementation. Then, the proposed method is compared with the other state‐of‐the‐art clustering techniques with three performance metrics: the silhouette coefficient, the Davies–Bouldin index, and the Calinski–Harabasz index. This article's main contribution is analyzing six different scenarios (different angles of the problem) concerning grievous and non‐injury accidents. This analysis provides deeper insights into the problem and can assist transport authorities in Tamil Nadu, India, in deriving new rules for road traffic. The output of different scenarios is compared with hierarchical clustering, and the overall clustering of the proposed method is compared with other clustering algorithms. Finally, it is proven that the proposed method outperforms other recently developed techniques.

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.

Road Accidents: The Most Influential Severity and Risk Factors

Many countries all over the world, greatly concern about the growth of mortality and injury rate on the road. Understanding the factors affecting road traffic accident is an important area for a road safety research. The aim of this paper is to present the overview of factors influencing the road traffic accident severity. From the review of literature, the mostly mentioned factors that are found to be significant to road traffic accident severity are speed of vehicle traveled, followed by human characteristics. Other factors that are found to be significant are vehicle types, weather, alcohol consumption, driver’s fatigue, and etc.

Traffic safety analysis for different road junctions in the Latvian national road network in the period from 2017-2019

Traffic organization must be safe, but according to traffic accident data, the highest number is at intersections. Different intersections are evaluated based on various criteria - road throughput, conflict points, number and severity of road traffic accidents. There are many different types of intersections in the road network, each of them is suitable for certain road parameters in the road network. In the main road network of the State of Latvia for the period 2017-2019, data was collected and places where road traffic accidents often occur were evaluated. Where one of the most important tasks was to find a rational way to improve safety without losing the importance of the road. The aim of this work is to analyze the impact of different intersections on the road network, as well as to provide recommendations for a safe consistent road network based on actual data.

Analysis and Detection of Road Traffic Accident Severity via Data Mining Techniques: Case Study Addis Ababa, Ethiopia

Around the world, road traffic accidents are the leading cause of serious injuries and deaths. Ethiopia is one of the countries that suffer the most from traffic accidents. Every government in every country wants to keep its citizens safe from accidents. To keep people safe from accidents, it is necessary to conduct a detailed analysis of the factors that contribute to high‐severity accidents and deaths. As a result, we developed a data mining algorithm‐based road traffic accident severity analysis for the Addis Ababa subcity in this study. The longest frequent factors in the dataset were generated using the Apriori algorithm. The Apriori algorithm generates the most frequent factors as sex: male, driver–vehicle relationship: employee, weather condition: normal, pedestrian movement: not a pedestrian, road surface type: asphalt, and accident severity: high severity, with 42.21% and 84.35% support and confidence, respectively. In addition, we created an accident severity level predictive model using a support vector machine. The predictive model has an accuracy of 85%. The proposed predictive model outperforms other well‐known predictive models, such as K‐nearest neighbors, decision trees, and random forests. As a result, when making decisions or policies in Ethiopia, the government or private organizations should consider the association of factors that lead to serious severe accidents.

Comparison of Statistical and Machine-Learning Models on Road Traffic Accident Severity Classification

Portugal has the sixth highest road fatality rate among European Union members. This is a problem of different dimensions with serious consequences in people’s lives. This study analyses daily data from police and government authorities on road traffic accidents that occurred between 2016 and 2019 in a district of Portugal. This paper looks for the determinants that contribute to the existence of victims in road traffic accidents, as well as the determinants for fatalities and/or serious injuries in accidents with victims. We use logistic regression models, and the results are compared to the machine-learning model results. For the severity model, where the response variable indicates whether only property damage or casualties resulted in the traffic accident, we used a large sample with a small imbalance. For the serious injuries model, where the response variable indicates whether or not there were victims with serious injuries and/or fatalities in the traffic accident with victims, we used a small sample with very imbalanced data. Empirical analysis supports the conclusion that, with a small sample of imbalanced data, machine-learning models generally do not perform better than statistical models; however, they perform similarly when the sample is large and has a small imbalance.

Road traffic accidents in Italy during COVID-19

Objective To assess changes in the number and severity of road traffic accidents in Italy in 2020, in particular after the beginning of COVID-19 and during the lockdown, as compared with 2019, with monthly details and geographical variations within the country. Methods Official monthly data on road traffic accidents recorded by the Police in Italy in 2020 were compared with those in 2019. The comparison regarded number of accidents, percent change, non-fatal injuries, deaths, injury index (injuries/accidents ×100) and fatality index (deaths/accidents ×100). Monthly data were graphically presented separately for each of the 21 Italian Regions and autonomous Provinces. Results A steep generalized decrease in the number of road traffic accidents was observed in March and April 2020 (Italian lockdown) as compared with the corresponding months of 2019 (more than 70% change), with a smaller change in the number of deaths, more variable among Regions. Smaller decreases were observed in the following part of 2020. Conclusions In Italy, lockdown and limitation of mobility due to COVID-19 determined a strong decrease in the number of road traffic accidents and their health consequences. Inter-regional variability in the decrease of deaths might be associated with the severity of the SARS-CoV-2 local outbreak, although specific causes need to be investigated. These data are useful to inform traffic and public health policy makers.

Analysis of 5-year monitoring of road traffic injuries rates in Kazan

The objective of the study is to establish a set of possible causes and patterns that influence the frequency and severity of road traffic accidents with fatalities. The results of the 5-year monitoring analysis of fatal road traffic injuries in Kazan are presented. We analyzed 662 cases. It was established that the main risk group for fatal road accidents includes subjects aged 20-39. The risk group for drivers is 30-39 years old, passengers 20-29 years old, and pedestrians 55-64 years old. Children account for 3% of all traffic fatalities; 80% of them are under 10 years old. Most children (70%) were in the vehicle at the time of the crash, and their parents may not have been using special equipment (car seats and seat belts). We identified the most traumatic months of the year, days of the week and times of the day for drivers, passengers and pedestrians. These patterns can be used for organizational measures to prevent road accidents.

Identifying the Causes and Protective Measures of Road Traffic Accidents (RTAs) in Bahawalpur City, Pakistan

Road Traffic Accident (RTA) is a growing public issue and fall among the four top causes of mortality and morbidity globally. The main objective of this study was to identify the causes and protective measures of road traffic accidents in Bahawalpur City. Primary data was gathered through a structured questionnaire during a field survey in selected five public places as sample sites i.e. Larry Ada, University Chowk, Bahawal Victoria Hospital (BVH), One Unit Chowk, and Melad Chowk. Secondary data of road accidents was gathered form National Highway and Motor Way Police (NH&MP) while primary data was gathered from 150 respondents (30 from each study site) and analyzed in SPSS software by applying descriptive statistics and road accident risk index (RARI). Findings revealed that the main causes of these accidents include increase in population (62.66%), increase in demand for vehicles (22%), bike drivers (69.33%), overtaking of the vehicles (51.33%), over speed and hustle to reach the destination (34.66%). One wheeling is also a major reason, which results in the death of teenage drivers (52%), violation of the traffic rules (25.33%). RARI results also suggest the relationship between the affected persons and the road traffic accidents. Lastly, few suggestions were proposed to overcome the ratio and severity of road traffic accidents because these accidents are predictable and largely preventable through multi-disciplinary coherent strategies.

ANALYZING THE FACTORS INFLUENCING ROAD TRAFFIC ACCIDENT SEVERITY: A CASE STUDY OF KHULNA CITY

Road traffic accident occurrences are terrible global phenomena all over the world. With more and different motorized vehicles, Bangladesh and Khulna city face a more significant number of fatalities and injuries. The metropolitan city is one of the riskiest places for road accidents because of its higher population density than in any other location. Road accidents in Khulna city are a significant problem, and they are increasing day by day. So, to reduce the severity of road accidents, the factors influencing road accident severity in Khulna city must be studied and analyzed. Binomial logistic regression is selected and applied to predict the risk or severity of 266 accidents from 2010 to 2019, collected by the Traffic Branch of the Deputy Police Commissioner of Khulna Metropolitan Police. The response variable for this study is accident severity (fatal and non-fatal). According to the study's findings, the number of injuries, number of fatalities, vehicle velocity, accident time, collision type, type of vehicle, and vehicle ownership influence the severity of road traffic accidents in Khulna. This study and its recommendations will aid in developing and implementing future road traffic policies and safety measures.

Road Traffic Safety Analysis Of Different Junction Types On The State Roads

The highest number of road accidents occurs at junctions. One of the aims of traffic organisation is to improve traffic safety in these areas. Based on a variety of indices – road capacity, points of conflict, number, and severity of road traffic accidents – different alternatives for junctions are evaluated. However, the road network has many junctions and roads serve to travel from point "A" to point "B" at a given time. Therefore, one of the most important tasks when addressing the issue of road safety is to find a rational way of improving the safety without losing the importance of the road. The aim of this paper is to analyse the impact of different junctions on the road network and basing on actual data develop a method for the evaluation of different types of junctions with respect to road class.

Factors Influencing for Severity of Road Traffic Accidents in Sri Lanka

The Sri Lankan Journal of Applied Statistics(SLJAS) is an open-access, international, double-blind peer-reviewed journal published by the Institute of Applied Statistics, Sri Lanka (IASSL). The main purpose of the journal is to publish the results of original work on applications of Statistics and on theoretical and methodical aspects of Statistics. The journal also welcomes critical reviews including conceptual discussions, opinions and book reviews. Applications of Statistics in the area of Agriculture & Forestry, Medical, Dental and Veterinary Sciences, Natural, Physical Sciences, Social Sciences, Economics and Actuarial Science fall within the scope of the journal. This journal does not charge any fee for article processing and submission.

A review on neural network techniques for the prediction of road traffic accident severity

Abstract The occurrence rate of death and injury due to road traffic accidents is rising increasingly globally day by day. For several decades, the focus of research has been on getting a deeper understanding of the significant factors that influence the risk of road traffic fatalities. In today's modern world, neural network (NN) approaches play a crucial role in identifying the contributing factors that describe the frequency and severity of road accidents. Over the years, many researchers used neural network models for predicting the impact of such factors on road accident injury severity. Deep learning methods such as the recurrent neural network (RNN) and the convolutional neural network (CNN) has recently been successfully used for the prediction of road accidents and demonstrate their high accuracy and efficiency. This study overview and summarizes the different forms of neural network models such as the single layer perceptron (SLP) neural network, the multilayer layer perceptron (MLP) neural network, the radial basis function (RBF) neural network, the recurrent neural network, and the convolutional neural network used as a prediction method for the severity of road crash injuries and includes a discussion of future planning and difficulties. This article also summarizes the model input parameter or independent variable and output or dependent variable, as well as various performance assessment methods.

Road accident prediction and model interpretation using a hybrid K-means and random forest algorithm approach

Road accident severity is a major concern of the world, particularly in underdeveloped countries. Understanding the primary and contributing factors may combat road traffic accident severity. This study identified insights and the most significant target specific contributing factors for road accident severity. To get the most determinant road accident variables, a hybrid K-means and random forest (RF) approaches developed. K-means extract hidden information from road accident data and creates a new feature in the training set. The distance between each cluster and the joining line of k1 and k9 calculated and selected maximum value as k. k is an optimal value for the partition of the training set. RF employed to classify severity prediction. After comparing with other classification techniques, the result revealed that among classification techniques, the proposed approach disclosed an accuracy of 99.86%. The target-specific model interpretation result showed that driver experience and day, light condition, driver age, and service year of the vehicle were the strong contributing factors for serious injury, light injury, and fatal severity, respectively. The outcome demonstrates the predictive supremacy of the approach in road accident prediction. Road transport and insurance agencies will be benefited from the study to develop road safety strategies.

Did the supreme court liquor shop ban in 2017 on highways impact the incidence and severity of road traffic accidents

Background: Alcohol intoxication plays a major role in the causation of road traffic accidents (RTAs) and assault. On April 1, 2017, the honorable Supreme Court of India banned liquor shops on all national and state highways to a distance of 500 m from them. Out of 231 liquor shops, 178 were closed in Vellore district after the ban. This, we assumed, would have reduced the number of drunken drivers and pedestrians, resulting in the fall in the number and severity of the accidents. Materials and Methods: This is a retrospective observational study done in a tertiary hospital in South India. All RTA patients presented to the emergency department (ED) 1 month before the liquor shop ban (March 2017) were included in the preban group and who presented to the ED 1 month after the liquor shop ban (April 2017) were included in the postban group; their incidence and severity of trauma were assessed and compared. A bivariate analysis was done to identify the relationship between these variables and potential determinants, and their 95% confidence interval (CI) was calculated. For all tests, a two-sided P

A hybrid optimization framework for road traffic accident data

With the exponential growth of the number of vehicles in the traffic, road traffic accidents have become a fast-growing and wide-spreading menace, causing the loss of precious human life and economic assets. In addition to the fast growth of population and motorization, roads are highly occupied by cars, buses, trucks, motorcycles, minibikes, taxis, pedestrians, animals, and other travelers. The main challenges in the road traffic accident data prediction and analysis is the small size of the dataset that can be used for training. While road traffic accident causes millions of deaths and injuries every year, their density in time and space is –fortunately-low. This makes machine learning very challenging at the local level. The main target of this work is to minimize the Causality Severity of road traffic accidents. Mathematical analysis for the attributes’ effect on Causality Severity was guided to select effective twenty decision variables. An optimization search requires a reliable simulated model that depicts the necessary characteristics of the system under study. The good formulation of the simulated model was reduced the non-linearity and interaction between the variables. The simulated model has been verified by successive linear technique with aid of the Successive Linear Programming (SLP). Mixed Integer nonlinear Program (MINLP) has proven a reliable optimization search with the present model. Reasonable agreements have found when compared the simulated solutions with the experimental data. From the output results, we observed that some of the attributes have a positive effect on Causality-Severity, while other attributes have a negative effect on it. In addition to the implementation output of both MINLP and SLP, we can state that the Age of Driver, Speed Limit, and Age of Causality are necessary and sensitive variables for objective Causality Severity. Optimal results would guide for selecting the best conditions.