Road traffic accident in Nigeria is increasing at a worrying rate and has raised one of the country major concerns. We provided appropriate and suitable time series model for the consequences of road accident, the injured, killed and total casualty of the road accident in Nigeria. The most widely used conventional method, Autoregressive Integrated Moving Average (ARIMA) model of time series, also known as Box-Jenkins method is applied to yearly data on the consequences of road accident data in Nigeria from 1960-2013 to determine patterns of road traffic accident consequences; injured, killed and total casualty of the road accident along the Nigeria motorway. Appropriate models are developed for the accident consequences; injured, killed and total casualty. ARIMA (0; 2; 1) model is obtained for the injury and total casualty consequences, whilst ARIMA(1,2,2) model is obtained for the killed consequences, using the data from 1960-2011. The adequacy and the performance of the model are tested on the remaining data from 2012 to 2013. Seven years forecast are provided using the developed models and showed that road traffic accident consequences examined; injured, killed and total casualty would continue to increase on average.
PREDICTION OF ROAD ACCIDENT MODELLING FOR INDIAN NATIONAL HIGHWAYSIAEME Publication
The objective of this research article is to identify the most critical safety influencing variables of a section of four-lane National Highway-18(old)/40(New) through statistical models that explains the relationship between frequency of accident count and highway safety variables. The Highway traverses mainly through a plain terrain of mostly agricultural areas. The study is for newly constructing Four-Lane road between chainage 224.000 (Chagalamarri) to 359.9(Kurnool) to identify all safety deficiencies responsible for road accidents. The predictive ability using Multiple linear regression model is under two categories: First for the 2 lane sections and second for 4 lane sections separately. The validation tools were applied to examine the ability of models to predict accidents.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Nowadays, road crashes become a growing worldwide problem and result in around 1 million deaths now occurs in developing countries. Huge economic losses are now being incurred annually in the ASEAN countries as a direct result of road crashes and the most recent research suggests annual losses across the region are now in excess of US dollar 14 billion per year (around 2.1% of annual GDP of ASEAN region). In Myanmar, thousands of healthy lives are lost by road accidents comparing with other ASEAN countries. A research was conducted on a section of Pyay road with its high-accident locations to study and evaluate the cause of its frequent accidents. Initial study indicated that most of the accidents were attributed to human elements. This was included by the fact that a high percentage of accident was caused by the collision of moving vehicle and pedestrian. Identifying and removing hazardous spots to improve road safety will primarily requires well documented record on those roads with high-accident locations. These data base can inform to urban transport planner for road safety improvement. Kyaing"Road Accident Study on Some Areas in Yangon" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-5 , August 2018, URL: http://www.ijtsrd.com/papers/ijtsrd15944.pdf http://www.ijtsrd.com/engineering/civil-engineering/15944/road-accident-study-on-some-areas-in-yangon/kyaing
PREDICTION OF ROAD ACCIDENT MODELLING FOR INDIAN NATIONAL HIGHWAYSIAEME Publication
The objective of this research article is to identify the most critical safety influencing variables of a section of four-lane National Highway-18(old)/40(New) through statistical models that explains the relationship between frequency of accident count and highway safety variables. The Highway traverses mainly through a plain terrain of mostly agricultural areas. The study is for newly constructing Four-Lane road between chainage 224.000 (Chagalamarri) to 359.9(Kurnool) to identify all safety deficiencies responsible for road accidents. The predictive ability using Multiple linear regression model is under two categories: First for the 2 lane sections and second for 4 lane sections separately. The validation tools were applied to examine the ability of models to predict accidents.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Nowadays, road crashes become a growing worldwide problem and result in around 1 million deaths now occurs in developing countries. Huge economic losses are now being incurred annually in the ASEAN countries as a direct result of road crashes and the most recent research suggests annual losses across the region are now in excess of US dollar 14 billion per year (around 2.1% of annual GDP of ASEAN region). In Myanmar, thousands of healthy lives are lost by road accidents comparing with other ASEAN countries. A research was conducted on a section of Pyay road with its high-accident locations to study and evaluate the cause of its frequent accidents. Initial study indicated that most of the accidents were attributed to human elements. This was included by the fact that a high percentage of accident was caused by the collision of moving vehicle and pedestrian. Identifying and removing hazardous spots to improve road safety will primarily requires well documented record on those roads with high-accident locations. These data base can inform to urban transport planner for road safety improvement. Kyaing"Road Accident Study on Some Areas in Yangon" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-5 , August 2018, URL: http://www.ijtsrd.com/papers/ijtsrd15944.pdf http://www.ijtsrd.com/engineering/civil-engineering/15944/road-accident-study-on-some-areas-in-yangon/kyaing
Accident Black Spot Identification | KJEI Campus to Chandni ChowkShadaab Sayyed
The seminar by Rutuja Gawade, Amol Pawar, Swapnil Borge, Nazim Ansari under the guidance of Prof. Jitesh Dhule about the accidents black spot identification along the route.
4Data Mining Approach of Accident Occurrences Identification with Effective M...IJECEIAES
Data mining is used in various domains of research to identify a new cause for tan effect in the society over the globe. This article includes the same reason for using the data mining to identify the Accident Occurrences in different regions and to identify the most valid reason for happening accidents over the globe. Data Mining and Advanced Machine Learning algorithms are used in this research approach and this article discusses about hyperline, classifications, pre-processing of the data, training the machine with the sample datasets which are collected from different regions in which we have structural and semi-structural data. We will dive into deep of machine learning and data mining classification algorithms to find or predict something novel about the accident occurrences over the globe. We majorly concentrate on two classification algorithms to minify the research and task and they are very basic and important classification algorithms. SVM (Support vector machine), CNB Classifier. This discussion will be quite interesting with WEKA tool for CNB classifier, Bag of Words Identification, Word Count and Frequency Calculation.
Analysis of Machine Learning Algorithm with Road Accidents Data SetsDr. Amarjeet Singh
Beginning at now, street transport framework neglect to alter up to the exponential expansion in vehicular masses and to ascertaining the quickest driving courses and catastrophes inside observing differing traffic conditions is a critical issue right presently structures. To upset this issue is to explore the vehicle division dataset with bundle learning technique for finding the best street choice without calamity gauging by want aftereffects of best accuracy count by looking at oversaw AI figuring. In bits of information and AI, bundle strategies utilize diverse learning calculations to give indications of progress prudent execution. The assessment of dataset by facilitated AI technique (SMLT) to get two or three data takes after, factor perceiving proof, univariate evaluation, bivariate and multi-variate appraisal, missing worth medications and separate the information support, information cleaning/organizing and information perception will be done with everything taken into account given dataset. In addition, to look at and talk about the presentation of different AI figuring estimations from the given vehicle division dataset with assessment of GUI based street fiasco want by given attributes.
ANALYSIS OF ROADWAY FATAL ACCIDENTS USING ENSEMBLE-BASED META-CLASSIFIERSijaia
In the past decades, a lot of effort has been put into roadway traffic safety. With the help of data mining, the analysis of roadway traffic data is much needed to understand the factors related to fatal accidents. This paper analyses Fatality Analysis Reporting System (FARS) dataset using several data mining
algorithms. Here, we compare the performance of four meta-classifiers and four data-oriented techniques known for their ability to handle imbalanced datasets, entirely based on Random Forest classifier. Also, we study the effect of applying several feature selection algorithms including PSO, Cuckoo, Bat and Tabu on improving the accuracy and efficiency of classification. The empirical results show that the Threshold
selector meta-classifier combined with over-sampling techniques results were very satisfactory. In this regard, the proposed technique has gained a mean overall Accuracy of 91% and a Balanced Accuracy that varies between 96% to 99% using 7-15 features instead of 50 original features.
A Basic Frame Work For Formulation Of Road Safety Improvement Programijcite
Road accidents have increased and reached to an alarming point in past
few years in most of the developing countries including India. The
continued steep increase in the number of road accidents indicates that
these losses are undoubtedly inhibiting the economic and social
development of the countries and adding to the poverty and hardships of
the poor. Thus, there is an urgent need to take preventive measures to
reduce accidents and to develop road safety improvement program. This
study proposes a basic frame work for formulation of road safety
improvement program. The framework consist of four major stages i.e
Stage I ranking of safety hazardous location, stage II evaluation of
safety hazardous condition at different Section in road network, Stage III
prioritization of remedial safety measures and Stage IV selection of
safety measures (formulation of road safety improvement program) .
This study also presents the basic concepts to develop a methodology for
formulation of road safety improvement program. Thus, this study will
be useful for researcher and policy makers to develop a methodology for
formulation of road safety improvement program to select remedial
safety measures to improve safety on hazardous location in the road
network
International Refereed Journal of Engineering and Science (IRJES)irjes
International Refereed Journal of Engineering and Science (IRJES) is a leading international journal for publication of new ideas, the state of the art research results and fundamental advances in all aspects of Engineering and Science. IRJES is a open access, peer reviewed international journal with a primary objective to provide the academic community and industry for the submission of half of original research and applications
The Effects of Vehicle Speeds on Accident Frequency within Settlements along ...IJMER
Literature provides overwhelming evidence that a strong relationship exist between
vehicle speed and accident risk, and an outcome severity in the event of an accident. Excessive speed
is said to be a major causal factor of road accidents on trunk roads; contributing 60% of all vehicular
accidents. However, speed rationalization measures implemented on a number of trunk roads in
Ghana have realized very little success. This study therefore investigated the effects of vehicle speeds
on accident frequency within settlements along trunk roads. Data was collected on accidents, vehicle
speeds and other road and environment-related features for ninety-nine (99) settlements delineated
from four (4) trunk roads. Correlation analysis was employed to establish useful relationships and
provided insight into the contributions of relevant road and environmental-related variables to the
occurrence of road traffic accidents. Using the Negative Binomial error structure within the
Generalized Linear Model framework, core (flow-based) models were formulated based on accident
data and exposure variables (vehicle mileage, daily pedestrian flow and travel speed). Incremental
addition of relevant explanatory variables further expanded the core models into comprehensive
models. Findings indicate the main risk factors are number of accesses, daily pedestrian flow and
total vehicle kilometers driven, as vehicle speed did not appear to influence the occurrence of road
traffic accidents within settlements along trunk roads. In settlement corridors, mitigating accident
risks should not focus only on traffic calming but rather on measures that reduce pedestrian and
vehicular conflict situations as well as improve conspicuity around junctions
Study On Traffic Conlict At Unsignalized Intersection In Malaysia IOSR Journals
The research conducted is traffic conflict at unsignalized intersections . The purpose of this research
is to study accident data used as an identification of hazardous location leads to less accurate countermeasures.
It is because accidents are not always reported especially accident involving damage only and this situation can
reduce good comparative analysis. To overcome these lacks of accident data, many ways of employing nonaccident
data have been suggested. One of the ways using non-accident data is traffic conflicts, which is defined
as critical incidents not necessarily involving collisions. The traffic conflict technique was originally set up to
provide more reliable data and information of traffic problems at intersections which actually would replace the
unclear and incomplete recorded data accident. The conflict study was done at the selected unsignalized
intersection where types of traffic conflict can be identified and classified. Various road users involved in the
conflict at the unsignalized intersection were also observed. Then conflicts data captured were analyzed using
the computer program to observe for any conflicts at the intersections. The linear regression graph was used to
show the relationship between conflict and accident data where two different equations were derived from the
graph. This equation may be used to make a prediction for the relationship that might exist between those two
variables at another location.
PREDICTING ROAD ACCIDENT RISK USING GOOGLE MAPS IMAGES AND ACONVOLUTIONAL NEU...ijaia
Location specific characteristics of a road segment such as road geometry as well as surrounding road features can contribute significantly to road accident risk. A Google Maps image of a road segment provides a comprehensive visual of its complex geometry and the surrounding features. This paper proposes a novel machine learning approach using Convolutional Neural Networks (CNN) to accident risk prediction by unlocking the precise interaction of these many small road features that work in combination to contribute to a greater accident risk. The model has worldwide applicability and a very low cost/time effort to implement for a new city since Google Maps are available in most places across the globe. It also significantly contributes to existing research on accident prevention by allowing for the inclusion of highly detailed road geometry to weigh in on the prediction as well as the new locationbased attributes like proximity to schools and businesses.
The fatality of traffic accidents of the world population is approximately 1.2 million people every year. According to the World Health Organization(2004), related injuries from road incidents will rank 3rd for global burden of disease in 2030. In order to tackle traffic accidents effectively, one needs to analyse their traffic pattern. The traffic accident black spot programme is developed from analysis of traffic accidents (Chris’s Britain Road Directory, 2017). Black spot or black site refers to area with high traffic accident risk. In 1955, the UK first introduced an unprecedented type of traffic sign – Accident Black Spot Sign (The National Archives, 2017). Since then, more and more Commonwealth countries followed the UK to promote and develop their own black spot investigations. In this paper, I will first explain why traffic accidents occurs and common determination methods of black spots. After that, I will present the current situation of Hong Kong.
Towards Improving Crash Data Management System in Gulf CountriesIJERA Editor
Scientific and analytical approaches to accident data collection, storage and analysis are essential in dealing with road safety problems. Police accident records in the majority of countries form the main (and sometimes the only) source of accident data. Access to the accident database is also important to identifying specific safety problems and evaluating the effectiveness of the countermeasure introduced. Accident data collection and analysis offered by technological innovation such as Electronic Data Entry (EDE), Electronic Data transfer (EDT), and Geographic Information system (GIS) are implemented in developed countries. Developing countries, including the Gulf countries, should take advantage of the experience of developed countries on how the advance accident data management system works to identifying, more accurately, the main factors contributing to traffic accident. The main purpose of this research is to provide information on accident statistics process in Virginia state, starting from the time of accident occurring until it is stored in the database, with the aim of using it towards improving the process of collecting and maintaining accident data system in Gulf countries. The task is performed by reviewing the relevant international literature and interviewing police officers in charge and academic researchers in order to compare the accident data management system and also the quality of the data. Recommendations towards developing the crash data management system will be obtained based on the research results and international experience.
Accident Black Spot Identification | KJEI Campus to Chandni ChowkShadaab Sayyed
The seminar by Rutuja Gawade, Amol Pawar, Swapnil Borge, Nazim Ansari under the guidance of Prof. Jitesh Dhule about the accidents black spot identification along the route.
4Data Mining Approach of Accident Occurrences Identification with Effective M...IJECEIAES
Data mining is used in various domains of research to identify a new cause for tan effect in the society over the globe. This article includes the same reason for using the data mining to identify the Accident Occurrences in different regions and to identify the most valid reason for happening accidents over the globe. Data Mining and Advanced Machine Learning algorithms are used in this research approach and this article discusses about hyperline, classifications, pre-processing of the data, training the machine with the sample datasets which are collected from different regions in which we have structural and semi-structural data. We will dive into deep of machine learning and data mining classification algorithms to find or predict something novel about the accident occurrences over the globe. We majorly concentrate on two classification algorithms to minify the research and task and they are very basic and important classification algorithms. SVM (Support vector machine), CNB Classifier. This discussion will be quite interesting with WEKA tool for CNB classifier, Bag of Words Identification, Word Count and Frequency Calculation.
Analysis of Machine Learning Algorithm with Road Accidents Data SetsDr. Amarjeet Singh
Beginning at now, street transport framework neglect to alter up to the exponential expansion in vehicular masses and to ascertaining the quickest driving courses and catastrophes inside observing differing traffic conditions is a critical issue right presently structures. To upset this issue is to explore the vehicle division dataset with bundle learning technique for finding the best street choice without calamity gauging by want aftereffects of best accuracy count by looking at oversaw AI figuring. In bits of information and AI, bundle strategies utilize diverse learning calculations to give indications of progress prudent execution. The assessment of dataset by facilitated AI technique (SMLT) to get two or three data takes after, factor perceiving proof, univariate evaluation, bivariate and multi-variate appraisal, missing worth medications and separate the information support, information cleaning/organizing and information perception will be done with everything taken into account given dataset. In addition, to look at and talk about the presentation of different AI figuring estimations from the given vehicle division dataset with assessment of GUI based street fiasco want by given attributes.
ANALYSIS OF ROADWAY FATAL ACCIDENTS USING ENSEMBLE-BASED META-CLASSIFIERSijaia
In the past decades, a lot of effort has been put into roadway traffic safety. With the help of data mining, the analysis of roadway traffic data is much needed to understand the factors related to fatal accidents. This paper analyses Fatality Analysis Reporting System (FARS) dataset using several data mining
algorithms. Here, we compare the performance of four meta-classifiers and four data-oriented techniques known for their ability to handle imbalanced datasets, entirely based on Random Forest classifier. Also, we study the effect of applying several feature selection algorithms including PSO, Cuckoo, Bat and Tabu on improving the accuracy and efficiency of classification. The empirical results show that the Threshold
selector meta-classifier combined with over-sampling techniques results were very satisfactory. In this regard, the proposed technique has gained a mean overall Accuracy of 91% and a Balanced Accuracy that varies between 96% to 99% using 7-15 features instead of 50 original features.
A Basic Frame Work For Formulation Of Road Safety Improvement Programijcite
Road accidents have increased and reached to an alarming point in past
few years in most of the developing countries including India. The
continued steep increase in the number of road accidents indicates that
these losses are undoubtedly inhibiting the economic and social
development of the countries and adding to the poverty and hardships of
the poor. Thus, there is an urgent need to take preventive measures to
reduce accidents and to develop road safety improvement program. This
study proposes a basic frame work for formulation of road safety
improvement program. The framework consist of four major stages i.e
Stage I ranking of safety hazardous location, stage II evaluation of
safety hazardous condition at different Section in road network, Stage III
prioritization of remedial safety measures and Stage IV selection of
safety measures (formulation of road safety improvement program) .
This study also presents the basic concepts to develop a methodology for
formulation of road safety improvement program. Thus, this study will
be useful for researcher and policy makers to develop a methodology for
formulation of road safety improvement program to select remedial
safety measures to improve safety on hazardous location in the road
network
International Refereed Journal of Engineering and Science (IRJES)irjes
International Refereed Journal of Engineering and Science (IRJES) is a leading international journal for publication of new ideas, the state of the art research results and fundamental advances in all aspects of Engineering and Science. IRJES is a open access, peer reviewed international journal with a primary objective to provide the academic community and industry for the submission of half of original research and applications
The Effects of Vehicle Speeds on Accident Frequency within Settlements along ...IJMER
Literature provides overwhelming evidence that a strong relationship exist between
vehicle speed and accident risk, and an outcome severity in the event of an accident. Excessive speed
is said to be a major causal factor of road accidents on trunk roads; contributing 60% of all vehicular
accidents. However, speed rationalization measures implemented on a number of trunk roads in
Ghana have realized very little success. This study therefore investigated the effects of vehicle speeds
on accident frequency within settlements along trunk roads. Data was collected on accidents, vehicle
speeds and other road and environment-related features for ninety-nine (99) settlements delineated
from four (4) trunk roads. Correlation analysis was employed to establish useful relationships and
provided insight into the contributions of relevant road and environmental-related variables to the
occurrence of road traffic accidents. Using the Negative Binomial error structure within the
Generalized Linear Model framework, core (flow-based) models were formulated based on accident
data and exposure variables (vehicle mileage, daily pedestrian flow and travel speed). Incremental
addition of relevant explanatory variables further expanded the core models into comprehensive
models. Findings indicate the main risk factors are number of accesses, daily pedestrian flow and
total vehicle kilometers driven, as vehicle speed did not appear to influence the occurrence of road
traffic accidents within settlements along trunk roads. In settlement corridors, mitigating accident
risks should not focus only on traffic calming but rather on measures that reduce pedestrian and
vehicular conflict situations as well as improve conspicuity around junctions
Study On Traffic Conlict At Unsignalized Intersection In Malaysia IOSR Journals
The research conducted is traffic conflict at unsignalized intersections . The purpose of this research
is to study accident data used as an identification of hazardous location leads to less accurate countermeasures.
It is because accidents are not always reported especially accident involving damage only and this situation can
reduce good comparative analysis. To overcome these lacks of accident data, many ways of employing nonaccident
data have been suggested. One of the ways using non-accident data is traffic conflicts, which is defined
as critical incidents not necessarily involving collisions. The traffic conflict technique was originally set up to
provide more reliable data and information of traffic problems at intersections which actually would replace the
unclear and incomplete recorded data accident. The conflict study was done at the selected unsignalized
intersection where types of traffic conflict can be identified and classified. Various road users involved in the
conflict at the unsignalized intersection were also observed. Then conflicts data captured were analyzed using
the computer program to observe for any conflicts at the intersections. The linear regression graph was used to
show the relationship between conflict and accident data where two different equations were derived from the
graph. This equation may be used to make a prediction for the relationship that might exist between those two
variables at another location.
PREDICTING ROAD ACCIDENT RISK USING GOOGLE MAPS IMAGES AND ACONVOLUTIONAL NEU...ijaia
Location specific characteristics of a road segment such as road geometry as well as surrounding road features can contribute significantly to road accident risk. A Google Maps image of a road segment provides a comprehensive visual of its complex geometry and the surrounding features. This paper proposes a novel machine learning approach using Convolutional Neural Networks (CNN) to accident risk prediction by unlocking the precise interaction of these many small road features that work in combination to contribute to a greater accident risk. The model has worldwide applicability and a very low cost/time effort to implement for a new city since Google Maps are available in most places across the globe. It also significantly contributes to existing research on accident prevention by allowing for the inclusion of highly detailed road geometry to weigh in on the prediction as well as the new locationbased attributes like proximity to schools and businesses.
The fatality of traffic accidents of the world population is approximately 1.2 million people every year. According to the World Health Organization(2004), related injuries from road incidents will rank 3rd for global burden of disease in 2030. In order to tackle traffic accidents effectively, one needs to analyse their traffic pattern. The traffic accident black spot programme is developed from analysis of traffic accidents (Chris’s Britain Road Directory, 2017). Black spot or black site refers to area with high traffic accident risk. In 1955, the UK first introduced an unprecedented type of traffic sign – Accident Black Spot Sign (The National Archives, 2017). Since then, more and more Commonwealth countries followed the UK to promote and develop their own black spot investigations. In this paper, I will first explain why traffic accidents occurs and common determination methods of black spots. After that, I will present the current situation of Hong Kong.
Towards Improving Crash Data Management System in Gulf CountriesIJERA Editor
Scientific and analytical approaches to accident data collection, storage and analysis are essential in dealing with road safety problems. Police accident records in the majority of countries form the main (and sometimes the only) source of accident data. Access to the accident database is also important to identifying specific safety problems and evaluating the effectiveness of the countermeasure introduced. Accident data collection and analysis offered by technological innovation such as Electronic Data Entry (EDE), Electronic Data transfer (EDT), and Geographic Information system (GIS) are implemented in developed countries. Developing countries, including the Gulf countries, should take advantage of the experience of developed countries on how the advance accident data management system works to identifying, more accurately, the main factors contributing to traffic accident. The main purpose of this research is to provide information on accident statistics process in Virginia state, starting from the time of accident occurring until it is stored in the database, with the aim of using it towards improving the process of collecting and maintaining accident data system in Gulf countries. The task is performed by reviewing the relevant international literature and interviewing police officers in charge and academic researchers in order to compare the accident data management system and also the quality of the data. Recommendations towards developing the crash data management system will be obtained based on the research results and international experience.
Analysis of Roadway Fatal Accidents using Ensemble-based Meta-Classifiersgerogepatton
In the past decades, a lot of effort has been put into roadway traffic safety. With the help of data mining,
the analysis of roadway traffic data is much needed to understand the factors related to fatal accidents.
This paper analyses Fatality Analysis Reporting System (FARS) dataset using several data mining
algorithms. Here, we compare the performance of four meta-classifiers and four data-oriented techniques
known for their ability to handle imbalanced datasets, entirely based on Random Forest classifier. Also,
we study the effect of applying several feature selection algorithms including PSO, Cuckoo, Bat and Tabu
on improving the accuracy and efficiency of classification. The empirical results show that the Threshold
selector meta-classifier combined with over-sampling techniques results were very satisfactory. In this
regard, the proposed technique has gained a mean overall Accuracy of 91% and a Balanced Accuracy that
varies between 96% to 99% using 7-15 features instead of 50 original features.
International Refereed Journal of Engineering and Science (IRJES)irjes
International Refereed Journal of Engineering and Science (IRJES) is a leading international journal for publication of new ideas, the state of the art research results and fundamental advances in all aspects of Engineering and Science. IRJES is a open access, peer reviewed international journal with a primary objective to provide the academic community and industry for the submission of half of original research and applications
This work discusses the study and development of a graphical interface and implementation of a machine learning model for vehicle traffic injury and fatality prediction for a specified date range and for a certain zip (US postal) code based on the New York City's (NYC) vehicle crash data set. While previous studies focused on accident causes, little insight has been offered into how such data may be utilized to forecast future incidents, Studies that have historically concentrated on certain road segment types, such as highways and other streets, and a specific geographic region, this study offers a citywide review of collisions. Using cutting-edge database and networking technology, a user-friendly interface was created to display vehicle crash series. Following this, a support vector machine learning model was built to evaluate the likelihood of an accident and the consequent injuries and deaths at the zip code level for all of NYC and to better mitigate such events. Using the visualization and prediction approach, the findings show that it is efficient and accurate. Aside from transportation experts and government policymakers, the machine learning approach deliver useful insights to the insurance business since it quantifies collision risk data collected at specific places.
International Refereed Journal of Engineering and Science (IRJES)irjes
International Refereed Journal of Engineering and Science (IRJES) is a leading international journal for publication of new ideas, the state of the art research results and fundamental advances in all aspects of Engineering and Science. IRJES is a open access, peer reviewed international journal with a primary objective to provide the academic community and industry for the submission of half of original research and applications
International Refereed Journal of Engineering and Science (IRJES)irjes
International Refereed Journal of Engineering and Science (IRJES) is a leading international journal for publication of new ideas, the state of the art research results and fundamental advances in all aspects of Engineering and Science. IRJES is a open access, peer reviewed international journal with a primary objective to provide the academic community and industry for the submission of half of original research and applications
EVALUATION OF PARTICLE SWARM OPTIMIZATION ALGORITHM IN PREDICTION OF THE CAR ...ijcsa
Road traffic accidents are the most common accidents that annually Endangers lives of many people in the world. Our country Iran is one of the countries with highest incidence and mortality due to accidents that has been introduced. So it’s requires identification of underlay in dimensions in this field. Due to the increasing amount of car accidents in order to increase volume of information related to car accidents and needs to explore and reveal hidden dependencies and very long time among this information. So using traditional methods to discover these complex relations don't response between involved factors and we need to use new techniques. Considering that main aim of this paper is to find best relationship between volumes of information in shortest time. So, in this paper, we classify accidents in West Azerbaijan province in Iran by accident type (damage, injury, death) and we describe it by using Particle Swarm Optimization (PSO) algorithm
Vehicle accidents are by all accounts appalling and frightening occasions occurring which cause various deaths. As the number of accidents per year is increasing tremendously and so the lives affected by accidents. There are traditional ways to help the needy or the victim that is informing the right authority but needs assistance or help from others, but this tends to take ample of time and due to it could cost lives. So there is a need to develop an accident detection system that would detect and alert the proper authorities about the accident. The sudden assistance to the alert would in return lead to saving as many lives as possible. Many researchers have analyzed this technique using Convolutional neural network, HDNN, RCNN, etc. This paper will give us an overview of various techniques or methods that are used to detect accidents.
Black spots identification on rural roads based on extremelearning machineIJECEIAES
Accident black spots are usually defined as road locations with a high risk of fatal accidents. A thorough analysis of these areas is essential to determine the real causes of mortality due to these accidents and can thus help anticipate the necessary decisions to be made to mitigate their effects. In this context, this study aims to develop a model for the identification, classification and analysis of black spots on roads in Morocco. These areas are first identified using extreme learning machine (ELM) algorithm, and then the infrastructure factors are analyzed by ordinal regression. The XGBoost model is adopted for weighted severity index (WSI) generation, which in turn generates the severity scores to be assigned to individual road segments. The latter are then classified into four classes by using a categorization approach (high, medium, low and safe). Finally, the bagging extreme learning machine is used to classify the severity of road segments according to infrastructures and environmental factors. Simulation results show that the proposed framework accurately and efficiently identified the black spots and outperformed the reputable competing models, especially in terms of accuracy 98.6%. In conclusion, the ordinal analysis revealed that pavement width, road curve type, shoulder width and position were the significant factors contributing to accidents on rural roads.
PREDICTING ACCIDENT SEVERITY: AN ANALYSIS OF FACTORS AFFECTING ACCIDENT SEVER...IJCI JOURNAL
Road accidents have significant economic and societal costs, with a small number of severe accidents
accounting for a large portion of these costs. Predicting accident severity can help in the proactive
approach to road safety by identifying potential unsafe road conditions and taking well-informed
actions to reduce the number of severe accidents. This study investigates the effectiveness of the
Random Forest machine learning algorithm for predicting the severity of an accident. The model is
trained on a dataset of accident records from a large metropolitan area and evaluated using various
metrics. Hyperparameters and feature selection are optimized to improve the model's performance.
The results show that the Random Forest model is an effective tool for predicting accident severity with
an accuracy of over 80%. The study also identifies the top six most important variables in the model,
which include wind speed, pressure, humidity, visibility, clear conditions, and cloud cover. The fitted
model has an Area Under the Curve of 80%, a recall of 79.2%, a precision of 97.1%, and an F1 score
of 87.3%. These results suggest that the proposed model has higher performance in explaining the
target variable, which is the accident severity class. Overall, the study provides evidence that the
Random Forest model is a viable and reliable tool for predicting accident severity and can be used to
help reduce the number of fatalities and injuries due to road accidents in the United States.
Text Mining in Digital Libraries using OKAPI BM25 ModelEditor IJCATR
The emergence of the internet has made vast amounts of information available and easily accessible online. As a result, most libraries have digitized their content in order to remain relevant to their users and to keep pace with the advancement of the internet. However, these digital libraries have been criticized for using inefficient information retrieval models that do not perform relevance ranking to the retrieved results. This paper proposed the use of OKAPI BM25 model in text mining so as means of improving relevance ranking of digital libraries. Okapi BM25 model was selected because it is a probability-based relevance ranking algorithm. A case study research was conducted and the model design was based on information retrieval processes. The performance of Boolean, vector space, and Okapi BM25 models was compared for data retrieval. Relevant ranked documents were retrieved and displayed at the OPAC framework search page. The results revealed that Okapi BM 25 outperformed Boolean model and Vector Space model. Therefore, this paper proposes the use of Okapi BM25 model to reward terms according to their relative frequencies in a document so as to improve the performance of text mining in digital libraries.
Green Computing, eco trends, climate change, e-waste and eco-friendlyEditor IJCATR
This study focused on the practice of using computing resources more efficiently while maintaining or increasing overall performance. Sustainable IT services require the integration of green computing practices such as power management, virtualization, improving cooling technology, recycling, electronic waste disposal, and optimization of the IT infrastructure to meet sustainability requirements. Studies have shown that costs of power utilized by IT departments can approach 50% of the overall energy costs for an organization. While there is an expectation that green IT should lower costs and the firm’s impact on the environment, there has been far less attention directed at understanding the strategic benefits of sustainable IT services in terms of the creation of customer value, business value and societal value. This paper provides a review of the literature on sustainable IT, key areas of focus, and identifies a core set of principles to guide sustainable IT service design.
Policies for Green Computing and E-Waste in NigeriaEditor IJCATR
Computers today are an integral part of individuals’ lives all around the world, but unfortunately these devices are toxic to the environment given the materials used, their limited battery life and technological obsolescence. Individuals are concerned about the hazardous materials ever present in computers, even if the importance of various attributes differs, and that a more environment -friendly attitude can be obtained through exposure to educational materials. In this paper, we aim to delineate the problem of e-waste in Nigeria and highlight a series of measures and the advantage they herald for our country and propose a series of action steps to develop in these areas further. It is possible for Nigeria to have an immediate economic stimulus and job creation while moving quickly to abide by the requirements of climate change legislation and energy efficiency directives. The costs of implementing energy efficiency and renewable energy measures are minimal as they are not cash expenditures but rather investments paid back by future, continuous energy savings.
Performance Evaluation of VANETs for Evaluating Node Stability in Dynamic Sce...Editor IJCATR
Vehicular ad hoc networks (VANETs) are a favorable area of exploration which empowers the interconnection amid the movable vehicles and between transportable units (vehicles) and road side units (RSU). In Vehicular Ad Hoc Networks (VANETs), mobile vehicles can be organized into assemblage to promote interconnection links. The assemblage arrangement according to dimensions and geographical extend has serious influence on attribute of interaction .Vehicular ad hoc networks (VANETs) are subclass of mobile Ad-hoc network involving more complex mobility patterns. Because of mobility the topology changes very frequently. This raises a number of technical challenges including the stability of the network .There is a need for assemblage configuration leading to more stable realistic network. The paper provides investigation of various simulation scenarios in which cluster using k-means algorithm are generated and their numbers are varied to find the more stable configuration in real scenario of road.
Optimum Location of DG Units Considering Operation ConditionsEditor IJCATR
The optimal sizing and placement of Distributed Generation units (DG) are becoming very attractive to researchers these days. In this paper a two stage approach has been used for allocation and sizing of DGs in distribution system with time varying load model. The strategic placement of DGs can help in reducing energy losses and improving voltage profile. The proposed work discusses time varying loads that can be useful for selecting the location and optimizing DG operation. The method has the potential to be used for integrating the available DGs by identifying the best locations in a power system. The proposed method has been demonstrated on 9-bus test system.
Analysis of Comparison of Fuzzy Knn, C4.5 Algorithm, and Naïve Bayes Classifi...Editor IJCATR
Early detection of diabetes mellitus (DM) can prevent or inhibit complication. There are several laboratory test that must be done to detect DM. The result of this laboratory test then converted into data training. Data training used in this study generated from UCI Pima Database with 6 attributes that were used to classify positive or negative diabetes. There are various classification methods that are commonly used, and in this study three of them were compared, which were fuzzy KNN, C4.5 algorithm and Naïve Bayes Classifier (NBC) with one identical case. The objective of this study was to create software to classify DM using tested methods and compared the three methods based on accuracy, precision, and recall. The results showed that the best method was Fuzzy KNN with average and maximum accuracy reached 96% and 98%, respectively. In second place, NBC method had respective average and maximum accuracy of 87.5% and 90%. Lastly, C4.5 algorithm had average and maximum accuracy of 79.5% and 86%, respectively.
Web Scraping for Estimating new Record from Source SiteEditor IJCATR
Study in the Competitive field of Intelligent, and studies in the field of Web Scraping, have a symbiotic relationship mutualism. In the information age today, the website serves as a main source. The research focus is on how to get data from websites and how to slow down the intensity of the download. The problem that arises is the website sources are autonomous so that vulnerable changes the structure of the content at any time. The next problem is the system intrusion detection snort installed on the server to detect bot crawler. So the researchers propose the use of the methods of Mining Data Records and the method of Exponential Smoothing so that adaptive to changes in the structure of the content and do a browse or fetch automatically follow the pattern of the occurrences of the news. The results of the tests, with the threshold 0.3 for MDR and similarity threshold score 0.65 for STM, using recall and precision values produce f-measure average 92.6%. While the results of the tests of the exponential estimation smoothing using ? = 0.5 produces MAE 18.2 datarecord duplicate. It slowed down to 3.6 datarecord from 21.8 datarecord results schedule download/fetch fix in an average time of occurrence news.
Evaluating Semantic Similarity between Biomedical Concepts/Classes through S...Editor IJCATR
Most of the existing semantic similarity measures that use ontology structure as their primary source can measure semantic similarity between concepts/classes using single ontology. The ontology-based semantic similarity techniques such as structure-based semantic similarity techniques (Path Length Measure, Wu and Palmer’s Measure, and Leacock and Chodorow’s measure), information content-based similarity techniques (Resnik’s measure, Lin’s measure), and biomedical domain ontology techniques (Al-Mubaid and Nguyen’s measure (SimDist)) were evaluated relative to human experts’ ratings, and compared on sets of concepts using the ICD-10 “V1.0” terminology within the UMLS. The experimental results validate the efficiency of the SemDist technique in single ontology, and demonstrate that SemDist semantic similarity techniques, compared with the existing techniques, gives the best overall results of correlation with experts’ ratings.
Semantic Similarity Measures between Terms in the Biomedical Domain within f...Editor IJCATR
The techniques and tests are tools used to define how measure the goodness of ontology or its resources. The similarity between biomedical classes/concepts is an important task for the biomedical information extraction and knowledge discovery. However, most of the semantic similarity techniques can be adopted to be used in the biomedical domain (UMLS). Many experiments have been conducted to check the applicability of these measures. In this paper, we investigate to measure semantic similarity between two terms within single ontology or multiple ontologies in ICD-10 “V1.0” as primary source, and compare my results to human experts score by correlation coefficient.
A Strategy for Improving the Performance of Small Files in Openstack Swift Editor IJCATR
This is an effective way to improve the storage access performance of small files in Openstack Swift by adding an aggregate storage module. Because Swift will lead to too much disk operation when querying metadata, the transfer performance of plenty of small files is low. In this paper, we propose an aggregated storage strategy (ASS), and implement it in Swift. ASS comprises two parts which include merge storage and index storage. At the first stage, ASS arranges the write request queue in chronological order, and then stores objects in volumes. These volumes are large files that are stored in Swift actually. During the short encounter time, the object-to-volume mapping information is stored in Key-Value store at the second stage. The experimental results show that the ASS can effectively improve Swift's small file transfer performance.
Integrated System for Vehicle Clearance and RegistrationEditor IJCATR
Efficient management and control of government's cash resources rely on government banking arrangements. Nigeria, like many low income countries, employed fragmented systems in handling government receipts and payments. Later in 2016, Nigeria implemented a unified structure as recommended by the IMF, where all government funds are collected in one account would reduce borrowing costs, extend credit and improve government's fiscal policy among other benefits to government. This situation motivated us to embark on this research to design and implement an integrated system for vehicle clearance and registration. This system complies with the new Treasury Single Account policy to enable proper interaction and collaboration among five different level agencies (NCS, FRSC, SBIR, VIO and NPF) saddled with vehicular administration and activities in Nigeria. Since the system is web based, Object Oriented Hypermedia Design Methodology (OOHDM) is used. Tools such as Php, JavaScript, css, html, AJAX and other web development technologies were used. The result is a web based system that gives proper information about a vehicle starting from the exact date of importation to registration and renewal of licensing. Vehicle owner information, custom duty information, plate number registration details, etc. will also be efficiently retrieved from the system by any of the agencies without contacting the other agency at any point in time. Also number plate will no longer be the only means of vehicle identification as it is presently the case in Nigeria, because the unified system will automatically generate and assigned a Unique Vehicle Identification Pin Number (UVIPN) on payment of duty in the system to the vehicle and the UVIPN will be linked to the various agencies in the management information system.
Assessment of the Efficiency of Customer Order Management System: A Case Stu...Editor IJCATR
The Supermarket Management System deals with the automation of buying and selling of good and services. It includes both sales and purchase of items. The project Supermarket Management System is to be developed with the objective of making the system reliable, easier, fast, and more informative.
Energy-Aware Routing in Wireless Sensor Network Using Modified Bi-Directional A*Editor IJCATR
Energy is a key component in the Wireless Sensor Network (WSN)[1]. The system will not be able to run according to its function without the availability of adequate power units. One of the characteristics of wireless sensor network is Limitation energy[2]. A lot of research has been done to develop strategies to overcome this problem. One of them is clustering technique. The popular clustering technique is Low Energy Adaptive Clustering Hierarchy (LEACH)[3]. In LEACH, clustering techniques are used to determine Cluster Head (CH), which will then be assigned to forward packets to Base Station (BS). In this research, we propose other clustering techniques, which utilize the Social Network Analysis approach theory of Betweeness Centrality (BC) which will then be implemented in the Setup phase. While in the Steady-State phase, one of the heuristic searching algorithms, Modified Bi-Directional A* (MBDA *) is implemented. The experiment was performed deploy 100 nodes statically in the 100x100 area, with one Base Station at coordinates (50,50). To find out the reliability of the system, the experiment to do in 5000 rounds. The performance of the designed routing protocol strategy will be tested based on network lifetime, throughput, and residual energy. The results show that BC-MBDA * is better than LEACH. This is influenced by the ways of working LEACH in determining the CH that is dynamic, which is always changing in every data transmission process. This will result in the use of energy, because they always doing any computation to determine CH in every transmission process. In contrast to BC-MBDA *, CH is statically determined, so it can decrease energy usage.
Security in Software Defined Networks (SDN): Challenges and Research Opportun...Editor IJCATR
In networks, the rapidly changing traffic patterns of search engines, Internet of Things (IoT) devices, Big Data and data centers has thrown up new challenges for legacy; existing networks; and prompted the need for a more intelligent and innovative way to dynamically manage traffic and allocate limited network resources. Software Defined Network (SDN) which decouples the control plane from the data plane through network vitalizations aims to address these challenges. This paper has explored the SDN architecture and its implementation with the OpenFlow protocol. It has also assessed some of its benefits over traditional network architectures, security concerns and how it can be addressed in future research and related works in emerging economies such as Nigeria.
Measure the Similarity of Complaint Document Using Cosine Similarity Based on...Editor IJCATR
Report handling on "LAPOR!" (Laporan, Aspirasi dan Pengaduan Online Rakyat) system depending on the system administrator who manually reads every incoming report [3]. Read manually can lead to errors in handling complaints [4] if the data flow is huge and grows rapidly, it needs at least three days to prepare a confirmation and it sensitive to inconsistencies [3]. In this study, the authors propose a model that can measure the identities of the Query (Incoming) with Document (Archive). The authors employed Class-Based Indexing term weighting scheme, and Cosine Similarities to analyse document similarities. CoSimTFIDF, CoSimTFICF and CoSimTFIDFICF values used in classification as feature for K-Nearest Neighbour (K-NN) classifier. The optimum result evaluation is pre-processing employ 75% of training data ratio and 25% of test data with CoSimTFIDF feature. It deliver a high accuracy 84%. The k = 5 value obtain high accuracy 84.12%
Hangul Recognition Using Support Vector MachineEditor IJCATR
The recognition of Hangul Image is more difficult compared with that of Latin. It could be recognized from the structural arrangement. Hangul is arranged from two dimensions while Latin is only from the left to the right. The current research creates a system to convert Hangul image into Latin text in order to use it as a learning material on reading Hangul. In general, image recognition system is divided into three steps. The first step is preprocessing, which includes binarization, segmentation through connected component-labeling method, and thinning with Zhang Suen to decrease some pattern information. The second is receiving the feature from every single image, whose identification process is done through chain code method. The third is recognizing the process using Support Vector Machine (SVM) with some kernels. It works through letter image and Hangul word recognition. It consists of 34 letters, each of which has 15 different patterns. The whole patterns are 510, divided into 3 data scenarios. The highest result achieved is 94,7% using SVM kernel polynomial and radial basis function. The level of recognition result is influenced by many trained data. Whilst the recognition process of Hangul word applies to the type 2 Hangul word with 6 different patterns. The difference of these patterns appears from the change of the font type. The chosen fonts for data training are such as Batang, Dotum, Gaeul, Gulim, Malgun Gothic. Arial Unicode MS is used to test the data. The lowest accuracy is achieved through the use of SVM kernel radial basis function, which is 69%. The same result, 72 %, is given by the SVM kernel linear and polynomial.
Application of 3D Printing in EducationEditor IJCATR
This paper provides a review of literature concerning the application of 3D printing in the education system. The review identifies that 3D Printing is being applied across the Educational levels [1] as well as in Libraries, Laboratories, and Distance education systems. The review also finds that 3D Printing is being used to teach both students and trainers about 3D Printing and to develop 3D Printing skills.
Survey on Energy-Efficient Routing Algorithms for Underwater Wireless Sensor ...Editor IJCATR
In underwater environment, for retrieval of information the routing mechanism is used. In routing mechanism there are three to four types of nodes are used, one is sink node which is deployed on the water surface and can collect the information, courier/super/AUV or dolphin powerful nodes are deployed in the middle of the water for forwarding the packets, ordinary nodes are also forwarder nodes which can be deployed from bottom to surface of the water and source nodes are deployed at the seabed which can extract the valuable information from the bottom of the sea. In underwater environment the battery power of the nodes is limited and that power can be enhanced through better selection of the routing algorithm. This paper focuses the energy-efficient routing algorithms for their routing mechanisms to prolong the battery power of the nodes. This paper also focuses the performance analysis of the energy-efficient algorithms under which we can examine the better performance of the route selection mechanism which can prolong the battery power of the node
Comparative analysis on Void Node Removal Routing algorithms for Underwater W...Editor IJCATR
The designing of routing algorithms faces many challenges in underwater environment like: propagation delay, acoustic channel behaviour, limited bandwidth, high bit error rate, limited battery power, underwater pressure, node mobility, localization 3D deployment, and underwater obstacles (voids). This paper focuses the underwater voids which affects the overall performance of the entire network. The majority of the researchers have used the better approaches for removal of voids through alternate path selection mechanism but still research needs improvement. This paper also focuses the architecture and its operation through merits and demerits of the existing algorithms. This research article further focuses the analytical method of the performance analysis of existing algorithms through which we found the better approach for removal of voids
Decay Property for Solutions to Plate Type Equations with Variable CoefficientsEditor IJCATR
In this paper we consider the initial value problem for a plate type equation with variable coefficients and memory in
1 n R n ), which is of regularity-loss property. By using spectrally resolution, we study the pointwise estimates in the spectral
space of the fundamental solution to the corresponding linear problem. Appealing to this pointwise estimates, we obtain the global
existence and the decay estimates of solutions to the semilinear problem by employing the fixed point theorem
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
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Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdf
Consequences of Road Traffic Accident in Nigeria: Time Series Approach
1. International Journal of Computer Applications Technology and Research
Volume 4– Issue 4, 262 - 273, 2015, ISSN:- 2319–8656
www.ijcat.com 262
CONSEQUENCES OF ROAD TRAFFIC ACCIDENT
IN NIGERIA: TIME SERIES APPROACH
F.B. Adebola
Department of Statistics
Federal University of
Technology Akure.
Nigeria.
Ridwan A Sanusi
Department of Mathematics
and Statistics
King Fahd University of
Petroleum and Minerals,
Saudi Arabia.
N.A. Adegoke
Department of Statistics
Federal University of
Technology Akure.
Nigeria.
Abstract: Road traffic accident in Nigeria is increasing at a worrying rate and has raised one of the country major concerns. We
provided appropriate and suitable time series model for the consequences of road accident, the injured, killed and total casualty of the
road accident in Nigeria. The most widely used conventional method, Autoregressive Integrated Moving Average (ARIMA) model of
time series, also known as Box-Jenkins method is applied to yearly data on the consequences of road accident data in Nigeria from
1960-2013 to determine patterns of road traffic accident consequences; injured, killed and total casualty of the road accident along the
Nigeria motorway. Appropriate models are developed for the accident consequences; injured, killed and total casualty. ARIMA (0; 2;
1) model is obtained for the injury and total casualty consequences, whilst ARIMA(1,2,2) model is obtained for the killed
consequences, using the data from 1960-2011. The adequacy and the performance of the model are tested on the remaining data from
2012 to 2013. Seven years forecast are provided using the developed models and showed that road traffic accident consequences
examined; injured, killed and total casualty would continue to increase on average.
Keywords: ARIMA; forecast; injured; killed; casualty
1. INTRODUCTION
Road Traffic Accident occurs when there is collision of
vehicle with another vehicle, pedestrian and animals among
other, which at times result in injury, loss of property and
death. As mentioned in [11], road traffic accident leads to
approximately two million killed and approximately ten
million injuries annually. Also, an estimated value of 3000
people die in the world as a result of road traffic accidents
daily. A prediction of global leading causes of killed from
2008 to 2030 by World Health Organization revealed that, if
current trends and patterns continue, road traffic accidents
will increase from ninth to fifth of world leading cause of
killed 3.6% of global killed, up from 2.2% in 2004 [11].
While, disability-adjusted life years will rise from ninth with
2.7% of total disability-adjusted life in 2004 to third and 4.9%
of total disability-adjusted life in 2030 [10].
Nigeria, the most populous black country, has the highest rate
of mortality from road accidents in the world according to
statistics compiled by the Federal Road Safety Commission
(FRSC). The country leads 43 other nations with killed in
10,000 vehicle crashes. Ethiopia ranked second with 219
killed per 10,000 vehicles while Malawi, took the third
position and Ghana took the fourth position with 183 and 178
killed respectively [1].
Road traffic accidents is one of the leading causes of death
among older children and economically active adults between
the ages 30 and 49 years ([8];[9]; [6]). Considering the
importance of the road and the increased level of road traffic
accidents in recent years along the Nigeria roads, this study
aimed at characterizing the road traffic accident in Nigeria by
providing appropriate models that explain the consequences of
killed, injured and the total casualty from road accident in the
country so as to provide an enabling base for the development
of countermeasures by the government and the traffic control
agents to reduce incidences of road traffic accident on the
road.
Time series analysis encompases methods for analyzing data
ordered in time in order to develop appropriate model and
other characteristics of the time ordered data. It is commonly
used in the fields of business, economics, finance, agriculture
among others, as appropriate tool for model building. It
systematics examine the ordered data with the aim of studying
dynamic regularities that may enable forecasting future or
even controlling the variable, the forecast model will then be
used to predict future values based on previously observed
values. In theory, Auto-regressive Integrated Moving
Averages ARIMA Models are the most universal class of
models for forecasting a time series data. As proposed by Box
and Jenkins, that in general, forecasting based on ARIMA
models comprises of three different steps: Model
Identification, Parameter estimation and Diagnostic checking.
Until a desirable model for the data is identified, the three
steps will be repeated [3]. The method of Box and Jenkins
dictates an iterative process requiring a sound understanding
of time series analysis technique, some degree of judgement
and many rounds of trials [13].
Numerous works have been done on the analysis of Road
accidents. [5] examined road accidents in Kuwait, he used an
ARIMA model and compared it with ANN to predict killed in
Kuwait, he concluded that ANN was better in case of long
term series without seasonal fluctuations of accidents or
autocorrelations’ components. [4] used Bayesian Model for
ranking hazardous road sites, their model made use of all
relevant information per accident location, including the total
number of accidents and the number of killed, as well as the
number of slight and serious injuries. Moreover, the model
included the use of a cost function to rank the sites with
respect to their total expected cost to society.
2. International Journal of Computer Applications Technology and Research
Volume 4– Issue 4, 262 - 273, 2015, ISSN:- 2319–8656
www.ijcat.com 263
A procedure of Road Traffic Injury (RTI) in China by using
RTI data from 1951 to 2003 was established by [12]. A series
of predictive equations on RTI were established based on
ARIMA models. They concluded that time series models thus
established proves to be of significant usefulness in RTI
prediction. Two time series techniques; ARMA and Holt-
Winters (HW) algorithm to predict annual motor vehicle crash
killed were used by [7]. They concluded that the values
predicted by ARMA models are a little bit higher than the
ones obtained by HW algorithm. Intervention analysis with
univariate Box-Jenkins method to identify whether a change
in a particular policy had made an impact on the trends in
killed and fatality rates in Illinois was used [2]. He developed
ARIMA forecasting model for future trends in motorway
killed in an effort to provide assistance to policy development
in reducing fatality rates in Illinois.
Time series analysis have been used in many fields of research
and road safety is no exception. The results of this research
would also add to the many research works carried out in road
safety.
2. MATERIALS AND METHODS
Data used for the study is a secondary data, it was collected
on yearly basis from the office of the Federal Road Safety
Corps of Nigeria for the period 1960 to 2013. The data
represents the total number of registered consequences of
injuries, killed and total casualty for the period under study.
The Box and Jenkins approach for time series analysis was
employed for data analysis. According to Box and Jenkins,
as mentioned above, the steps include, the identification of
appropriate model for the data under study, estimation of
model parameters, model diagnostic and adequacy checking
and lastly, the model, if found appropriate would be used for
forecasting. Data from 1960 to 2011 are used for models
building, while, data from 2012 to 2013 are used for models
validation and forecast values of the best models for the
variables under study are obtained from 2014 to 2020.
Meanwhile, It is worth mentioning here that because of the
volume of the work, the best models out of several competing
models that explain the variables under study are only
included in the work.
3. MODEL BUILDING
The first step in model building is to obtain the time plot of
the data. This will give us an insight of the behaviour of the
series. Figures (1a, 1b, and 1c) show the time series plot of
injuries consequences, killed consequences and total casualty
from the total number of road accident in Nigeria.
The plots exhibit upward and downward movement for all the
three variables under study, with some significant upward and
downward trends at some parts of the series. The mean and
variance of the variables are not stable and varies with time.
The autocorrelation function of the studied variables has
shown in Figure (2a), Figure (2b) and Figure (3) describe the
correlation between values of the studied variables at different
points in time, as a function of the time difference. The first
several autocorrelations are persistently large and trailed off to
zero rather slowly for all the three variables and their spikes
also went of the autocorrelations limit at lag 13 the variables
under study.
Figure 1a: Time Series Plot of Injured Victims from Road
Accidents in Nigeria.
Figure 1b: Time Series Plots of killed from Road Accidents in
Nigeria.
Figures 1c: Time Series Plot of Total Casualty from Road
Accidents in Nigeria.
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The Augmented Dickey Fuller test as given in Figures (4a and
4b) and Figure (5) give a p-value of 0.91 for the injured
Figure 2a: Correlogram Plot of the Injured Victims Nigeria
Figure 2b: Correlogram Plot of the killed from Road
Accidents in Nigeria
Figure 3: Correlogram Plot of the Total Casualty from Road
Accidents in Nigeria.
Figure 4a: Unit Root Test of Injured consequences from Road
Accidents in Nigeria.
Figure 4b: Unit Root Test of killed from Road Accidents in
Nigeria.
victims, 0.6412 for the killed consequences and 0.8779 for the
total casualty, these indicate the presence of unit roots for the
series. All these aforementioned characteristics of the studied
variables show that the series are not stationary, thus require
differencing.
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Figure (5): Unit Root Test of total casualty from Road
Accidents in Nigeria.
Figures (6a, 6b, and 6c), show the second difference of the
studied variables, the series look more stable around the
mean, which shows that the variables are now stationary. All
the three variables become stationary after taken second non-
seasonal difference
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be justified, not ragged.
Figure 6a: Time Series Plot of the Second Difference for the
Injured Victims consequences of Road Accidents.
Figure 6b: Time Series Plot of the Second Difference for the
killed consequences of Road Accidents.
Figure 6c: Time Series Plot of the Second Difference for the
Injured Victims, killed and Total Casualty consequences of
Road Accidents.
Figure 7a: Correlogram Plot of the Second Difference for the
Injured Victims consequences of Road Accidents.
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Figure 7b: Correlogram Plots of the Second Difference for the
killed consequences of Road Accidents.
Figure 7c: Correlogram Plots of the Second Difference for the
Total Casualty consequences of Road Accidents.
The autocorrelation functions of the second difference for the
studied variables, has shown in Figures (7a, 7b, and 7c), also
confirm that the second difference are now stationary. Also,
the Augmented Dickey Fuller test as given in Figures (8a, 8b,
and 8c) gave a p-value of 0.000 for the Injured victims, 0.0004
for the killed consequences and 0.000 for the total casualty,
these also indicate the absence of unit roots in the series,
which confirm that the second differenced series are
stationary.
Figure 8a: Unit Root Test for the Second Difference for the
Injured Victims consequences of Road Accidents.
Figure 8b: Unit Root Tests for the Second Difference for the
killed consequences of Road Accidents.
Figure 8c: Unit Root Tests for the Second Difference for the
Total Casualty consequences of Road Accidents.
By comparing the autocorrelations functions with their error
limits, the only significant autocorrelations are at lag 1 for all
the three variables, that is, the autocorrelations cut off after
lag one which shows the existence of MA(1) behavior.
Similarly, the partial autocorrelations also cut off after lag one
for the injured consequences and total casualty, this indicates
the existence of AR(1) for the two variables (that is, injured
consequences and total casualty). Meanwhile, the partial
autocorrelation cuts off after lag two for the killed
consequences, which shows the existence of AR(1) and AR(2)
for the variable. Based on the features of the correlogram
plots of the stationary series, the following model in Figure
(1), are suggested.
Table 1: Suggested Models Based on the Correlogram Plots
Injured Victims killed Total Casualty
ARIMA(0,2,1) ARIMA(0,2,2) ARIMA(0,2,1)
ARIMA(1,2,0) ARIMA(1,2,2) ARIMA(1,2,0)
ARIMA(1,2,1) ARIMA(1,2,3) ARIMA(1,2,1)
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Each of the model is assessed based on its parameter
estimates, the corresponding diagnostics of the residuals, the
AIC and SIC in order to select the best model for forecasting
into the future. Meanwhile, out of all the competing models
that explain the variable of interest, the best models are;
ARIMA(0,2,1) for the Injured Victims consequences,
ARIMA(1,2,2) for killed consequences and ARIMA(0,2,1) for
the total casualty. The models are given in Figures (9a, 9b,
and 10).
Time Series Models for the Injured Victims, killed and
Total Casualty consequences of Road Accidents are given in
Figures (9a, 9b, and 10), the models coefficients are
significant and all the inverted AR roots satisfy the minimum
stationarity condition, the invertibility condition of MA is
satisfied and also. Also, the Durbin-Watson statistics is not far
from 2, which implies that there is no serial correlation in the
model residual, that is the model residual is not forecastable.
Figure 9a: Time Series Models for the Injured Victims
consequences of Road Accidents.
Figure 9b: Time Series Models for the killed consequences of
Road Accidents.
Figure 10: Time Series Models for the consequences of Road
Accidents
Also, all the Q-Stat of the correlogram plot of models
residuals are greater than 0.05 for the lags as given in Figures
(11a and 11b) and Figure (12), these imply that the model
residuals are White-Noise, that is adjacent observations are
not related (random) and which support the fact that the
models may be the appropriate models for the observed time
series.
Figure 11a: Correlogram Plot of the Residuals for the Injured
Victims killed of Road Accidents.
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Figure 11b: Correlogram Plot of the Residuals for the killed of
Road Accidents.
Figure 12: Correlogram Plot of the Total Casualty
consequences of Road Accidents.
Figure 13a: Unit Root Test for the Injured Victims
consequences of Road Accidents.
Figure 13b: Unit Root Test for the killed consequences of
Road Accidents.
Figure 13c: Unit Root Test for the Total Casualty
consequences of Road Accidents.
The unit roots tests of the models as given in Figures (13a,
13b, and 13c), show that the inverse roots of the models are
within a unit circle, which confirmed that the models in
Figures (9a, and 9b) and Figure (10) are stationary and
invertible. Thus, the models can be written as general linear
form
Figure 14a: Residual Plot for the Injured Victims
consequences of Road Accidents.
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Figure 14b: Residual Plot for the killed consequences of Road
Accidents.
Figure 14c: Residual Plot for the Total Casualty consequences
of Road Accidents.
The residual plots of the models as shown in Figures (14a,
14b, and 14c), also confirm that the models residuals are
random and non-forecastable, which implies that the models
are good.
Figures (15a, 15b, and 15c) gives the visual representation of
the original Injured consequences, killed consequences and
the Total casualty consequences, the data (blue line) and
confidence interval (red
Figure 15a: In-sample Forecast Graph for the Injured cases.
Figure 15b: In-sample Forecast Graph for the killed cases.
Figure 15c: In-sample Forecast Graph for the Total Casualties.
lines). The in-sample forecasts for the models fall within the
95% confidence Interval. Figures (16a, 16b, and 17) give the
in-sample models evaluations, the bias proportion and
variance proportion, which are used to check how far is the
forecast mean from the mean of the actual series and how far
is the forecast variance from the variance of the actual series
respectively are very close to zero and comparatively much
lower than the covariance proportion which measure the
remaining systematic forecast error. Note, the sum of the bias
proportion, variance proportion and the covariance proportion
is 1.
Figure 16a: In-sample Forecast Evaluation for the Injured
cases.
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Figure 16b: In-sample Forecast Evaluation for the killed
cases.
Figure 17: In-sample Forecast Evaluation for the
Total Casualties
3.1 MODEL VALIDATION
Table 2: Validation Table for ARIMA(0,2,1) Model of Injured
consequences.
Year
Injured
consequences
Forecast % Variation
2012 39348 42213.26 7.28%
2013 40057 43261.51 7.99%
After determining the best-fit model for the series and
estimating related parameters, the third phase of Box-Jenkins
fitting model was evaluated for series prediction. Using the
ARIMA (0,2,1) model, the model predicted that in 2012 an
approximately 42213.26 Injure consequences, this gives
7.28% percentage increase when compared with the real value
of 39348 Injured consequences. Also, the model predicted that
in 2013 an approximately 43261.51 Injure consequences, this
gives 7.99% percentage increament when compared with the
real value of 40057 Injured consequences as given in Table
(2).
Table 3: Validation Table for ARIMA(1,2,2) Model of Killed
consequences.
Year
killed
consequences
Forecast % Variation
2012 6092 6046.28 -0.75%
2013 6544 6236.27 -4.702%
Also, Table (3) gives the model validation for ARIMA (1,2,2)
model. The model predicted that in 2012 an approximately
6046.28 killed consequences of accident, this gives 0.75%
percentage decrease when compared with the real value of
6092 killed consequences. Also, the model predicted that in
2013 an approximately 6236.27 killed consequences, this
gives 4.702% percentage decrease when compared with the
real value of 6544 killed consequences.
Table 4: Validation Table for ARIMA(0,2,1) Model of Total
Casualty.
Lastly, Table (4) gives the model validation for ARIMA
(0,2,1) model. The model predicted that in 2012 an
approximately 46504.31 Total casualty consequences of
accident, this gives 2.34% percentage increase when
compared with the real value of 4544 total casualty
consequences. Also, the model predicted that in 2013 an
approximately 46838.61 total casualty consequences, this
gives 0.51% percentage increase when compared with the real
value of 46601 killed consequences.
Year
Total
Casualy
Forecast % Variation
2012 45440 46504.31 2.34%
2013 46601 46838.61 0.51%
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3.2 Models Forecasting
Table 5: Forecast Table for ARIMA(0,2,1) Model of Injured
consequences.
Table 6: Forecast Table for ARIMA(1,2,2) Model of killed
consequences.
Table 7: Forecast Table for ARIMA(0,2,1) Model of Total
Casualty consequences.
Year
Lower
Control
Limit
Forecast
Upper
Control
Limit
2014 32833.4 47415.9 61998.3
2015 30053.2 47145.8 64238.5
2016 27649.7 46679.6 65709.6
2017 26135.3 46897.7 67660.1
2018 24745.0 47274.3 69803.6
2019 22911.4 47098.1 71284.9
2020 21177.0 46794.0 72410.9
Figure 18a: Forecast Plot for the Injured cases.
Figure 18b: Forecast Plot for the killed cases.
Year
Lower
Control
Limit
Forecast
Upper
Control
Limit
2014 31660.6 44309.8 56959.2
2015 30375.7 45358.1 60340.4
2016 29235.8 46406.3 63576.8
2017 28186.1 47454.5 66722.9
2018 27195.4 48502.8 69810.2
2019 26243.8 49551.1 72858.3
2020 25318.2 50599.3 75880.4
Year
Lower
Control
Limit
Forecast
Upper
Control
Limit
2014 2775.5 6261.7 9747.9
2015 2299.2 6424.1 10549.0
2016 1853.8 6472.4 11091.0
2017 1481.3 6615.8 11750.2
2018 1107.5 6680.0 12252.5
2019 785.5 6810.1 12834.6
2020 455.6 6885.4 13315.2
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Figure 18c: Forecast Plot for the Total Casualties.
3.3 General Difference Form of the
Models.
The general difference of ARIMA (0,2,1); Injured
consequences is given as,
Yt = 2Yt−1 − Yt−2 + et − θ1et−1,
Yt = 2Yt−1 − Yt−2 + et − θ1et−1.
Substituting the value θ as given in Figure (9a), then the
model for the Injured consequences becomes,
Yt = 2Yt−1 − Yt−2 + et + 0.954388et−1.
Also, the general difference of ARIMA (1,2,2); killed
consequences is given as,
Yt = 2Yt−1 − Yt−2 + ψ1(Yt−1 − 2Yt−2 + Yt−3) + et − θ1et−1 −
θ2et−2,
but θ1 = 0,
Yt = (2 + ψ1)Yt−1 − (1 + 2ψ1)Yt−2 + ψ1Yt−3 + et − θ2et−2.
Substituting the values of ψ and θ as given in Figure (9b), then
the model for the killed consequences becomes,
Yt = 1.167534Yt−1 + 0.66492Yt−2 − 0.832466Yt−3 + et +
0.968707et−2.
Lastly, the general difference of ARIMA (0,2,1); total casualty
consequences is given as,
Yt = 2Yt−1 − Yt−2 + et − θ1et−1,
Yt = 2Yt−1 − Yt−2 + et − θ1et−1,
Substituting the value θ as given in Figure (10), then the
model for the total casualty consequences becomes,
Yt = 2Yt−1 − Yt−2 + et + 0.959777et−1.
4. Discussion
Road traffic accident in Nigeria is increasing at a worrying
and alarming rate and has raised one of the country major
concerns. Federal Road Safety Corps of Nigeria recognizes
the negative impacts of road safety accident and has
commended the positive contribution of road safety researches
as necessary tools to have significant accident initiatives. The
paper was carried out in order to identify the patterns of road
traffic accident consequences; injured, killed and total
casualty by developing appropriate time series ARIMA
models and predict 7 years consequences of road traffic
accident; injured, killed and total casualty along the Nigeria
motorway.
Time series analysis of the data from the years 1960-2013
showed that patterns of road traffic accident consequence;
injured; killed and total casualty are increasing along the
Nigeria motorway. The most widely used conventional
method of time series known as Autoregressive Integrated
Moving Average (ARIMA) model was applied to the annual-
consequence of road accident data in Nigeria from 1960-2013
to determine patterns of road traffic accident consequences;
injured, killed and total casualty of the road accident along the
Nigeria motorway. After identifying various tentative models
the appropriate models for the accident consequences; injured,
killed and total casualty. ARIMA (0,2,1) model was found to
be suitable model for the injury and total casualty
consequences, whilst ARIMA(1,2,2) model was found to be
suitable model for the killed consequences using the data from
1960-2011. The adequacy and performance of the model were
tested on the remaining data from 2012 to 2013.
We provided 7 years forecasts of the consequences of road
accident using the models developed and they showed that,
road traffic accident consequences examined; injured, killed
and total casualty will continue to increase. The study also
revealed that road traffic accident cases; injured and killed
along the motorway would continue to increase over the next
7 years. This study has provided reliable and genuine
information that could be useful for determining road accident
rate on Nigeria motorway and provide necessary prevention
for the unwanted act. The study will also be used for
providing important information in raising the level of
awareness among stakeholders in road safety, since the
problem has become a growing rife in Nigeria and also, be
useful in setting priorities when planning road traffic accident
interventions. Most Importantly, this study will provide
expected benefit to the road users, Federal Road Safety Corps,
researchers and other stakeholders in understanding the future
rate of the consequences of road accident.
5. RECOMMENDATION
We have derived appropriate ARIMA Models that explain the
behaviour and also the future patterns of the consequences of
Road Accident along motor highway in Nigeria. Meanwhile,
caution should be exercise in using the model, as it should not
be used beyond the forecasted period, this is mainly because
long time forecast may give arbitrary large forecast. Also,
appropriate laws should be made to caution drivers that over-
speed beyond the standard. Strict laws should be made to
enforce the use of seat-belt among the driver and also, the
passenger sitting in the front seat. This if enforced may reduce
the critical state of the accident.
The Federal Road Safety Corp (FRSC) and all the
stakeholders in charge of motorway in Nigeria should ensure
proper maintenance of the motorway, it should be maintained
in terms of the use of appropriate materials for patching pot
holes, provision of street lights to aid visibility in the night,
installation of traffic lights at new intersections created along
the road. Also, proper education should be made known to the
drivers on how to overtake on the motorway.
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Appropriate training and retraining of drivers should be
encourage towards reducing the carnage on over roads this
will greatly reduce the rate of road traffic accident in the
country. Road signals and signs that guide and instruct the
drivers on what is happening in some kilometers ahead should
always be made available on the motorway. Drivers should be
discourage from receiving or making calls while driving.
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