Predicting road traffic crash severity in Kaduna Metropolis using some selected machine learning techniques (original) (raw)
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Road traffic accidents are one of the leading causes of death and injuries in Zambia. Some of the answers to reducing the problem of road traffic accidents are through research, and data mining is one of the research tools for discovering the causes of road traffic accidents. The main aim of this study was to identify and investigate drivers, road, weather and motor Vehicle-related factors that contribute to the severity of a road traffic accident in Zambia. In this research, road traffic accident severity was classified into three classes and these are, fatal, seriously injured, and slightly injured. This research develops a road traffic accident prediction model and compares the performance of various prediction models in order to select the best performing algorithm in the prediction of the road traffic accident severity. The data used in this study was a data file collected from the Zambia Police Service headquarter in Lusaka. The data collected was from the year 2016 to 2020, it contained 159,698 road traffic accidents. The CRISP-DM 1.0 standard data mining methodology was adopted in this research. Using WEKA (Waikato Environment for Knowledge Analysis) data mining software, four renowned classification algorithms were engaged to model the severity of the accidents. These algorithms comprised of Decision Tree (J48), Rule Induction (PART), Naive Bayes, and Random Forest. To build the models, first the whole dataset was used as a training set for the algorithm and the same dataset was used to build classifiers using 10-fold cross-validation. To institute the main causal features for road accident severity, rules produced by the Decision Tree (J48) and PART algorithms were supplementary explored. The efficiency of the algorithms used in the research was evaluated by comparing the classification accuracy, the Receiver Operator Characteristics curve, and the results shown in the confusion matrix. The results showed that the Random forest algorithm performed better in terms of classification accuracy and produced a better Receiver Operator Characteristics curve using training set, while the J48 algorithm out-performed the other three algorithms in terms of classification accuracy using 10-fold cross-validation. The rules produced by PART algorithm shows that, year, province, tire condition, car braking condition, cause of the accident, driver's age, driver's license grade, time and lighting condition are the most important features in the classification of a road traffic accident severity.
Indonesian Journal of Electrical Engineering and Computer Science
This research was conducted to help the traffic policy makers and general public in preventing road incidents using the collected traffic accident dataset between the years 2016 and 2019. Data mining using classification algorithm was utilized to develop a predictive model for predicting occurrences of traffic accidents. Classification algorithms such as decision tree, k-nn, naïve bayes and neural network have been compared in identifying better classification capability in classifying stage of felony. Neural network shows a very promising result in classifying road accident with a total accuracy result of 87.63%. Nonetheless, k-nn and naïve bayes both acquired a higher than 80% accuracy which shows that this classification algorithms were also good in predicting road accidents. Moreover, public vehicle is more prone in accident rather than private vehicle in both stage of felony and accident may occur between or on 3:00pm and 6:00pm.
Prediction Model for Road Traffic Accident in Nigeria
Road Traffic Accidents (RTAs) are increasing with rapid pace and presently these are one of the leading causes of death in developing countries. Available data of auto road accident indicate that at least, 162 persons out of 100,000 Nigerians are regular victims of road accidents.This paper presents a predictive model for forecasting road traffic accident in Nigeria using Naïve Bayes'. Naive Bayes' classifier features of Waikato Environment for Knowledge Analysis software was used to formulate the model:. The data used consists of 600 road traffic accident data. The result of the prediction shows the system is reliable with 89.83% accuracy using selected dependent variables like the road condition, road dimension, human factor and the vehicular factors. In conclusion, this research presents a road traffic accident predictive model for forecasting road traffic accidents in Nigeria in order to prevent or reduce the occurence of road traffic accidents using naive bayes' model.
International Journal of Database Theory and Application, 2017
Road traffic accidents, the inadvertent crash involving at least one motor vehicle, occurring on a road open to public circulation, in which at least one person is injured or killed; intentional acts (murder, suicide) and natural disasters excluded, is indisputably one of the most frequent and most damaging calamities bedeviling human societies, in particular, Nigeria, today. It is therefore, of paramount importance to seek to identify the root causes of road traffic accidents in order to proffer mitigating solutions to address the menace. This research, aimed at predicting the likely causes of road accidents, its prone locations and time along Kano-Wudil highway in order to take all necessary counter measures is a step forward in this direction. In this study data mining decision tree algorithm was used to predict the causes of the accidents, its prone locations and time along Kano-Wudil Highway that links Kano State to Wudil Local Government Area Kano State for effective decision making.
The number of vehicles and road transportation increases rapidly daily. Hence the frequency of road accidents and crashes also gradually increase with it. Analysing traffic accidents is one of the essential concerns in the world. Due to the considerable number of casualties and fatalities caused by those accidents, taking necessary actions to reduce road accidents is a vital public safety concern and challenge worldwide. Various statistical methods and techniques are used to address this issue. Hence, those statistical implementations are used for multiple applications, such as extracting cause and effect to predict realtime accidents. In this study, a United States (US) Countrywide car accidents data set consisting of about 1.5 million accident records with other relevant 45 measurements related to the US Countrywide Traffic Accidents were used. This work aims to develop classification models that predict the likelihood of an accident is severe. In addition, this study also consists of descriptive analysis to recognise the key features affecting the accident severity. Supervised machine learning methods such as Decision tree, K-nearest neighbour, and Random forest were used to create classification models. The predictive model results show that the Random Forest model performs with an accuracy of 83.95% for the train set and 80.69% for the test set, proving that the Random forest model performs better in accurately detecting the most relevant factors describing a road accident severity.
2019
Objective: Analyze the road crashes in Cartagena (Colombia) and the factors associated with the collision and severity. The aim is to establish a set of rules for defining countermeasures to improve road safety. Methods: Data mining and machine learning techniques were used in 7894 traffic accidents from 2016 to 2017. The severity was determined between low (84%) and high (16%). Five classification algorithms to predict the accident severity were applied with WEKA Software (Waikato Environment for Knowledge Analysis). Including Decision Tree (DT-J48), Rule Induction (PART), Support Vector Machines (SVMs), Naive Bayes (NB), and Multilayer Perceptron (MLP). The effectiveness of each algorithm was implemented using cross-validation with 10-fold. Decision rules were defined from the results of the different methods. Results: The methods applied are consistent and similar in the overall results of precision, accuracy, recall, and area under the ROC curve. Conclusions: 12 decision rules w...
Prediction of Road Accidents Using Machine Learning Algorithms
Middle East Journal of Applied Science & Technology (MEJAST), 2023
Today, one of the top concerns for governments is road safety. There are many safety features built into cars, yet traffic accidents still happen frequently and are unavoidable. To lessen the harm caused by traffic accidents, predicting their causes has become the primary goal. In this situation, it will be beneficial to examine the frequency of accidents so that we can use this information to further aid us in developing strategies to lessen them. From this, we can deduce the connections between traffic accidents, road conditions, and the impact of environmental factors on accident occurrence. In order to construct an accident prediction model, I used machine learning techniques, including the Decision Tree, Random Forest, and Logistic Regression. The development of safety measures and accident prediction will both benefit from these classification systems. Several elements, including weather, vehicle condition, road surface condition, and light condition, can be used to predict road accidents. Three dataset files—accidents, casualties, and vehicles are loaded into this dataset. This allows us to forecast the severity of accidents.
Mathematical Problems in Engineering, 2015
With the ever-increasing number of vehicles on the road, traffic accidents have also increased, resulting in the loss of lives and properties, as well as immeasurable social costs. The environment, time, and region influence the occurrence of traffic accidents. The life and property loss is expected to be reduced by improving traffic engineering, education, and administration of law and advocacy. This study observed 2,471 traffic accidents which occurred in central Taiwan from January to December 2011 and used the Recursive Feature Elimination (RFE) of Feature Selection to screen the important factors affecting traffic accidents. It then established models to analyze traffic accidents with various methods, such as Fuzzy Robust Principal Component Analysis (FRPCA), Backpropagation Neural Network (BPNN), and Logistic Regression (LR). The proposed model aims to probe into the environments of traffic accidents, as well as the relationships between the variables of road designs, rule-vio...
Prediction of Road Traffic Accident in Nigeria Using Naive Baye's Approach
Road Traffic Accidents (RTAs) are increasing with rapid pace and presently these are one of the leading causes of death in developing countries. Available data of auto road accident indicate that at least, 162 persons out of 100,000 Nigerians are regular victims of road accidents.This paper presents a predictive model for forecasting road traffic accident in Nigeria using Naïve Bayes'. Naive Bayes' classifier features of Waikato Environment for Knowledge Analysis software was used to formulate the model:. The data used consists of 600 road traffic accident data. The result of the prediction shows the system is reliable with 89.83% accuracy using selected dependent variables like the road condition, road dimension, human factor and the vehicular factors. In conclusion, this research presents a road traffic accident predictive model for forecasting road traffic accidents in Nigeria in order to prevent or reduce the occurence of road traffic accidents using naive bayes' model.
Prediction of Traffic Accident Severity Using Data Mining Techniques in Ibb Province, Yemen
International Journal of Software Engineering and Computer Systems, 2019
Traffic accidents are the leading causes beyond death; it is the concern of most countries that strive for finding radical solutions to this problem. There are several methods used in the process of forecasting traffic accidents such as classification, assembly, association, etc. This paper surveyed the latest studies in the field of traffic accident prediction; the most important tools and algorithms were used in the prediction process such as Backpropagation Neural Networks and the decision tree. In addition, this paper proposed a model for predicting traffic accidents based on dataset obtained from the Directorate General of Traffic Statistics, Ibb, Yemen.