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Aklilu Elias Kurika et al., International Journal of Emerging Trends in Engineering Research, 8(9), September 2020, 5171 – 5176
5171
ABSTRACT
Vehicle traffic accident is one of the major agenda for the
government in which special attention has been given to
continuously reduce its occurrence and related risks. Wolaita
zone is one of the major areas in which increased vehicle
traffic accident occurs. Government and concerned bodies
have given special attention to reduce accident rate in the
country. By having this point as the motivating factor for
study, this work tried to predict factors of vehicle accidents by
using machine learning algorithms. We used unbalanced
datasets with 1611 instances, which was seven years data
from year 2012-2019. In order to analyze data and evaluate
patters of datasets, KDD process model was applied. The
learning algorithms applied for experiments were J48
decision tree, Random forest tree, Rep tree, Naïve Bayes and
Bayesian network classifiers. The experimental results,
model evaluation and performance measurement shows that
F-measure of J48 and Rep tree classifiers are comparatively
similar i.e. 97.87% and 97.80% respectively and Random
Forest tree performed less i.e. 90.9%. We identified the first
experiment of J48 tree as the best model by performance and
23 best rules were generated from this experiment; best
features were also identified. The most common victims, most
commonly participated vehicles in accident and black spot
areas for frequent accidents occurrences were identified. The
findings of this study are significant for road and traffic
authority and police commission for the revision and
endorsement of the rules, regulations and standards related to
traffic accidents; and therefore vehicle traffic accidents and
related risks can be reduced generally in our country Ethiopia
and specially at Wolaita Zone. We made accident data ready
for further analysis in order to get most important patterns of
datasets for any future researchers.
Key words : About four key words or phrases in alphabetical
order, separated by commas.
1. INTRODUCTION
Road or vehicle traffic accident is a universal problem [1] and
worldwide reports show that on average, more than four million
peoples die because of many reasons in one year. Among this
numbers, HIV AIDS and tuberculosis are the first and second cases
for the deaths and vehicle traffic accident is the third known case for
those dying on every day.
According to WHO and World Bank [2] in 2004, World Health Day,
organized by the World Health Organization for the first time be
devoted to Road Safety. Every year, according to the statistics, 1.2
million people are known to die in road accidents worldwide. The
study conducted on Guardian [3] also shows that in the 2020, vehicle
traffic accident will become the first factor that causes the death of
human beings in the world. More than half the people killed in
vehicle traffic crashes are young adults aged between 15 and 44
years often the breadwinners in a family. Furthermore, road traffic
injuries cost low income and middle-income countries between 1%
and 2% of their gross national product; more than the total
development aid received by these countries WHO and World Bank
[2]. A lot of researches were conducted on accidents from time to
time in every parts of the world to reduce the accident rate and they
used their own view on accident data according to their respective
areas and country perspectives.
Even though plenty of researches were conducted, vehicle traffic
accident increases rapidly and results in massive loss of humans’
life, materials damage and other equivalent losses. WHO and World
Bank [2] show that worldwide, an estimated 1.2 million people are
killed in road crashes each year and as many as 50 million are
injured. Projections indicate that these figures will increase by about
65% over the next 20 years unless there is new commitment to
prevention. The increased loses and related injuries cause various
problems to the economic development of respective countries.
According to the perspectives of different countries, there are
different kinds of attributes and contributing cases of the traffic
accidents. The accident risk factors are more over determined in the
developed countries and some preventive measures have been taken
to reduce the risk. But traffic accident risks, related material
damages and life lose increases from time to time in developing
countries. In Ethiopia, some researches has been conducted but the
risk factors cannot be reduced from time to time. In the case of
Predicting Factors of Vehicular Accidents using Machine
Learning Algorithm
Aklilu Elias Kurika
1
, Irfan Ahmad Ganie
2
, Yuliyanti Kadir
3
, Patrick D. Cerna
4
, Frice L. Desei
5
1Lecturer, Department of Information Technology, Wolaita Sodo University, Ethiopia,
akliluelias123@gmail.com
2 Irfan Ahmad Ganie, Department of Electrical Engineering, Indian Institute of Technology Jodhpur, India,
ganieirfan27@gmail.com
3Yuliyanti Kadir, Assistant Professor, Department of Civil Engineering, Universitas Negeri Gorontalo, Indonesia
4 Professor, Technology, Engineering and Research, PSHS-CRC, Philippines, pcerna@ieee.org
5 Assistant Professor, Department of Civil Engineering, Universitas Negeri Gorontalo, Indonesia
ISSN 2347 - 3983
Volume 8. No. 9, September 2020
International Journal of Emerging Trends in Engineering Research
Available Online at http://www.warse.org/IJETER/static/pdf/file/ijeter46892020.pdf
https://doi.org/10.30534/ijeter/2020/46892020
Aklilu Elias Kurika et al., International Journal of Emerging Trends in Engineering Research, 8(9), September 2020, 5171 – 5176
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Wolaita Zone, the timely recorded data realities on ground show that
traffic accident is the major issue that should be given special
attention. The reason is that the risks of traffic accidents and related
material and live loses show enormous increase from time to time.
But the reasons for increased traffic accident factors are not well
known. Additional deep analysis on accident data is indeed needed
and this is also a motivating factor to conduct study by machine
learning algorithms.
Generally the amount of data used by previous researchers is lesser;
some others used secondary data, which is collected by
questionnaire, as well as social media data for analysis. Using this
kind of data for predicting factors of traffic accident is not feasible.
Most of the studies that were conducted in the past literature are
mainly focus on J48 decision tree algorithms. Other kinds of decision
tree algorithms are not used for comparative analysis by most of the
researchers. Thus, performance comparisons have not been made for
more than two algorithms.
2. RELATED WORKS
Studies [5] and [4] are related to the locations of accident
related factors; accordingly the road features are one of the
contributing factors of traffic accidents. But the types of road
features are not clearly specified in these studies.
Studies performed by authors [6] and [7] are comparative
analysis in the performance measurement and accuracy of
algorithms. The first author compared six algorithms
(classification and regression tree, Random Forest, ID3,
Functional trees, Naïve Bayes and J48) algorithms to
determine the accidents severity level. It reveals that Naive
Bayes value and J48 techniques value are approximately same
in accuracy. The second one the comparative study on
machine learning algorithms; the comparison has been made
for decision tree and neural networks to determine factors of
increased traffic injury. It comes up with that the decision
trees are better than neural networks in performance.
Studies conducted by researchers explained in references [6],
[7], [8] identified the factors of traffic accidents; their
findings show that the causality factors are un- adopted
speech, in-attention, behavior of passengers, roadway
features, demographic features, environmental characters,
technical characters, speed, age, gender, younger aged
drivers, alcohol, less control, wrong over-taking and tire
blow. These factors were identified in various areas as the
contributing factors for the accidents. But it is impossible to
blindly take control over all these characteristics to be
considered in particular area. So accident factor analysis is
needed to identify the most commonly contributing factors
that hold a lion share of the commonly known determinant
attributes. Some of the factors are common in one area and
some other factors become common in other areas. While [9]
used social media data as the primary data for predicting the
causes of accident; secondary data is not suitable for analysis.
In Ethiopia, Wolaita zone is one of the most commonly
known areas in which traffic accidents and related injuries
take place. By analyzing the factors with learning algorithms,
the most contributing factors will be determined from traffic
accident data which is obtained from WZPC. Other
contributing factors other than these might also be obtained
for increased traffic accidents. The methodologies used by
various researchers are of various types. Akinbola et al., [10]
and [11] machine learning algorithms to predict the factors of
traffic accidents. Both of these authors used only decision
tree; and Tibebe et al., [12] is all about machine learning
algorithm but it is not for determining the causes of traffic
accidents and Gupta and Baluni [7] also used classification
and machine learning algorithms to determine traffic injury
occurrences.
3. MATERIALS AND METHODS
Classification algorithm has been identified as the best
technique to attain our objectives in accordance with
predetermined datasets we had. From various classification
algorithms, decision tree classifiers (J48, Random Forest and
Rep Tree) classifiers and from Bayesian classifiers (Naïve
Bayes and Bayesian Network) classifiers were selected to
conduct our experiments. We have computed 15 experiments,
(three for each classifiers i.e. by 10 fold cross validation, by
66% split and by 90% split for each of them respectively.) We
have identified 14 best features among 36 attributes with
wrapper method.
Knowledge discovery in datasets (KDD) process modeling
has been used as a study design based on Figure 1.
Figure 1: Knowledge discovery in datasets (KDD)
3.1 Data Integration:
To keep normal compliance of data, we integrated data to
common format according our objectives and identified most
important attributes to our study. Some of the attributes were
ignored from the original data because they are less
meaningful to our study. Accordingly, 36 important attributes
were identified and 1611data was prepared for analysis,
which is continuous 7 years data from 2005-2011 E.C. The
amount of data was limited to this number; because five years
(2000-2004) data was burned before it was being transformed
to police commission from road and transport authority.
Aklilu Elias Kurika et al., International Journal of Emerging Trends in Engineering Research, 8(9), September 2020, 5171 – 5176
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3.2 Data Selection
In order to get data for prediction, applicable data was
selected from 12 districts and three city administrations of
Wolaita Zone. The case study is limited to Wolaita zone only.
This is because we wanted to define the scope of our study.
3.3 Data Preprocessing:
In this step the data cleaning, data reduction and data
transformation has been made to prepare the best quality
datasets for further analysis. The original data was obtained
from Wolaita Zone police commission (PC) but, it has a lot of
drawbacks such as spelling errors, unreadable data, misspelt
attributes names, unknown values for some attributes and
irrelevant personal representations of some terms. Some
terms were inconsistent and considered to be outliers. We
removed irrelevant attributes from the original Data. In this
step we made the cleaning process of data before loading it to
WEKA.
3.4 Data Transformation:
The original data was recorded in word processor while some
data were in spreadsheet. The researcher transformed it to a
.svc format which the weka workbench can read and
supported.
Be aware of the different meanings of the homophones
“affect” (usually a verb) and “effect” (usually a noun),
“complement” and “compliment,” “discreet” and “discrete,”
“principal” (e.g., “principal investigator”) and “principle”
(e.g., “principle of measurement”). Do not confuse “imply”
and “infer.”
Prefixes such as “non,” “sub,” “micro,” “multi,” and “"ultra”
are not independent words; they should be joined to the words
they modify, usually without a hyphen. There is no period
after the “et” in the Latin abbreviation “et al.” (it is also
italicized). The abbreviation “i.e.,” means “that is,” and the
abbreviation “e.g.,” means “for example” (these abbreviations
are not italicized).
An excellent style manual and source of information for
science writers is [9].
4. EXPERIMENTATION
4.1 Most Prone Accident Vehicles
From the total 31 kinds of vehicles participated in accidents,
we have identified 7 kinds vehicles as the most commonly
participated. They account 75.34% and remaining 24 vehicles
participation is only 24.66%. So we can conclude that if these
vehicles were given separate road in cities specially Sodo-City
(>25%) traffic accident can be possibly reduced.
Figure 2 : Most Prone Accident Vehicles
4.2 Most Common Victims of Accidents
The above diagram shows that the most common victims of
accidents are pedestrians (40.16%) and passengers (19.93%).
Derives are less victims. So we can conclude that car traffic
accident most commonly affects pedestrians and passengers
in our case study. Males (53.8%) are most commonly affected
by car traffic accidents compared to females (19.6%); which
are opposite to study by [22] that revealed majority of
participants as females in accidents. 18.75% of victims were
aged between 1-18, 30.54% were aged between 19-30 and
18.56% were aged between 31-50.
Figure 3: Most Common Vehicular Accident Victims
As it is known, the most productive human power is aged
between 18 and 50. Therefore traffic accident affects the most
productive classes of humans as we can conclude from the
above result.
Aklilu Elias Kurika et al., International Journal of Emerging Trends in Engineering Research, 8(9), September 2020, 5171 – 5176
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4.3 Most Common Black Spot Areas
We have selected 19 places with frequent accident
occurrences from the above five Woredas. We selected areas
with > = 15 accidents within 7 years. From the total accidents
occurred, these places account 521 (32.34%) accidents. So
concerned bodies has to give attention to these areas.
Figure 4: Most Common Black Spot Areas
From 15 different areas shown above, the first five (Sodo-city,
Damot-Gale, Humbo, Sodo-Zuria and Boditi-City) account a
lot accidents i.e. 73.37% of total accidents. The remaining 10
districts account only 26.63%. Each of them accounts > 5%
accident occurrences from the total one, so we selected the
black spot areas for frequent accidents occurrences from these
five Woredas.
4.4 Determinant Cases of Accidents
The Most Determinant Cases and causality condition of
Accidents are: Lack of attention (65.49%), over speed
(10.62%), Prohibiting Priority (10.37%), lack of experience
(6.33%) and technic failure (3.54%). The causality condition
of accidents is mostly crossing the road (32.96%) straight
crash (28.80%), roll down (16.70%), side to side crash
(8.57%) and walking on the road (5.90%).
Figure 5: Determinant Cases of Accidents
Table 1: Summary of Experimental Results
As we can see from the above experimental results and below
diagram, J48 and Rep tree classifiers are comparatively
similar by their accuracy. We computed average Precision
and Recall of J48 and Rep tree and selected the J48 decision
tree algorithm as a better than Rep tree. 1st Expt J48 tree
Precision = 98% and Recall = 97.75%, (FM= 97.87%) 1st
Expt. Rep tree Precision = 97.70% and Recall = 97.90%,
(FM= 97.80%). The first experimental results of J48 decision
tree, includes more features than exp.2 and 3 even though the
number of leaves and size of tree generated are more. So we
selected it as a working model and generated 23 best rules
from this particular experiment.
Aklilu Elias Kurika et al., International Journal of Emerging Trends in Engineering Research, 8(9), September 2020, 5171 – 5176
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Figure 6: Diagrammatical representations of selected
experiments
Below are some of the best rules generated:
1. If Severity of Accident = Material Damage and Class of
Victims = Pedestrian and Time of Accident =
Morning/Evening Then Fatal in Accident: Yes.
2. If Severity of Accident = Material Damage and Class of
Victims = Pedestrian and Time of Accident = Night and
Number of Victims > 2: Then Fatal in Accident: Yes.
3. If Severity of Accident = Material Damage and Class of
Victims = Pedestrian and Time of Accident = Afternoon
and Type of Crashes = Vehicle With Pedestrian: Then Fatal
in Accident: No.
4. If Severity of Accident = Slight and Edu/n Level = Primary
and Settlement of Road = Upward and Type of Causality
Vehicle = Motor Cycle, ISUZU, ISUZU-Autobus, Minibus
Then Fatal in Accident: Yes.
5. If Severity of Accident = Slight and Edu/n Level = Primary
and Settlement of Road = Upward and Type of Crashed
Vehicle!= Motor Cycle Then Fatal in Accident: No.
4.5 Performance Measurement of Learning Algorithms
In the experiment evaluation part, we have identified that J48
and Rep tree are comparatively similar and better that the
remaining three classifiers. So we have used selected the first
and third experiments for each classifiers and measured
performance of their classifiers accuracy as follows.
Figure 7: Confusion Matrix
Since the dataset we have was unbalanced, taking accuracy of
the model to decide one model as best model is misleading. In
such cases, it is advisable to take precision and recall for
deciding whether one model is better than the other or not. In
our cases, four of the experiments listed above have
comparatively similar precision and recall values. But the 1st
and 7th experiments were computed by 10 fold cross
validation and the rest were computed by 90% split value for
training and testing the model. So model with good predictive
accuracy can be obtained by experiments performed with 10
fold cross validation tests according to expert judgments.
Then we ignored the rest experiments with 90% split tests and
accepted experiments with cross validation tests. Experiment
1st (98%) average precision and (97.75%) average recall for
two class labels and 7th experiment (97.70% ) average
precision and (97.90%) average recall were selected to
determine the best model with good predictive accuracy for
fatal and non-fatal accident occurrences.
Figure 8: Model Evaluations
The above result shows that J48 Tree and Rep tree are
significantly best by performance than all other classifiers
with the given dataset. Naïve Bayes and Bayesian network
classifiers are significantly good by their performance and the
rest two algorithms (Random forest and Random tree)
classifiers are poor by performance when compared to other
classifiers with the given dataset.
5. CONCLUSION
In this study, machine Learning approaches have been
applied for data analysis and prediction of car traffic accident
datasets to explore important features and pattern
relationships to car traffic accident occurrences. We
addressed various statements of problems and objectives to
determine determinant factors of car traffic accidents. We
identified 7 most commonly participating vehicles, 20 areas
for frequent accident occurrences, pedestrians and passengers
as the most common victims and J48 and Rep tree as best
algorithms by performance and model accuracy. 23 best rules
were generated from the selected model for accident
occurrences, results have been discussed and finally some
points have been recommended for the future researchers
Based on the outcomes of this study, the following points were
recommended for the future researchers. Comparatively
better results might be obtained if they try accident
predictions with techniques like support vector machine,
multilayer perceptron and artificial neural networks. Add
some unconsidered attributes to datasets and relate cases to
behavior of derivers like amount of alcohol taken and mental
normality of derivers to get better results. Try with deep
learning with large amount of instances to get better result
and integrate it with knowledge base to know cases for
accident occurrences to use is as an expert system.
Aklilu Elias Kurika et al., International Journal of Emerging Trends in Engineering Research, 8(9), September 2020, 5171 – 5176
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