EVALUVATING To give safe driving suggestions, clear and careful

EVALUVATING
ROAD TRAFFIC ACCIDENTS USING DATA

MINING
TECHNOLOGY

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ABSTRACT

Road
traffic safety is an important perturbation for government transport
authorities as well ascommon people. Road accidents are ambivalent and not able
to be predict the incidents. Andtheir survey requires the information affecting
them. Road accidents cause difficulties which are get bigger at an alarming
rate. Controlling the traffic accidents on roads is a crucial task. To give
safe driving suggestions, clear and careful study of roadway traffic data is
critical to find out the variables that are nearly to fatal accidents.
Increasing the number of vehicles from past few years has put lot of pressure
on the existing roads and ultimately resulting in increasing the road accidents.
A road traffic accident is any harm due to collision originating from,
terminating with or involving a vehicle partially or fully on a public road.

1.      INTRODUCTION

            In modern life, accidents have
become daily happening. Every day we hear the news of theaccident on the
television, or through internet .During accident many people die at the spot, some
others may injured very severely. By witnessing an accident one can understand
the horror of it. There are several reasons for road accidents, some of them
are increasing the number of vehicles, careless driving, violating traffic
rules etc. Whenever a road accident occur there are various types of damage
takes place ,which could be in the form of human beings, infrastructure which
is damage to the government and many other administration damages . Poor
roadway maintenance also contributes accidents. But still many people continue
to neglect and ignore the danger involved in the accidents. In this paper we
are analyzing some methods and algorithms to find out the problems occur in
road accidents.

Section
1 elucidate literature survey,Section 2 elucidate conclusion.

 

 

LITERATURE SURVEY

The paper 1 describes the association
rule mining, its classifications and the atmospheric components like roadway
surface, climate, and light condition do not strongly influence the fatal accident
rate. But the human factors like being alcoholic or not, and the impact have
strongly affect on the fatal accident rate. A common mechanism to recognize the
relations between the data stored in huge database and plays a very significant
role in repeated object set mining is association rule mining algorithm. A
classical association rule mining method is the Apriori algorithm whose main
aim is to identify repeated object sets to analyze the roadway traffic data.
Classification in data mining methodology focus atbuilding a classifier model
from a training data set that is used to classify records of unrevealed class
labels. The Naïve Bayes technique is one of the probability-based methods for
classification and is based on the Bayes’ hypothesis with the probability of
self-rule between every set of variables.The author applies statistics analysis and Fatal
Accident Reporting System
(FARS) tosolve this problem. From the clustering result some
regions have larger fatal rate but some others have smaller. When driving within
those risky or dangerous states,people take more attention. When the task
performed, data seems never to be sufficient to make a strong choice. If non-fatal
accident data, weather condition data, mileage data, and so on are available,
more test could be executed thus more advice could be made from the data.

In paper 2, K-modes clustering
techniqueis a framework that is used as an initialwork for divisionof different
road accidents on road network. Then association rule mining are used to
recognize the various situations that are related with the occurrence of an
accident for the entire data set (EDS) and the clusters recognized by K-modes
clustering algorithm. Six clusters (C1toC6) are used based on properties
accident type, road type, lightning on road and road feature identified by K
modes clustering method. On each cluster association rule mining is applied as
well as on EDS to create rules. Powerful methods with higher raise values are taken
for the inspection. Rules for various clusters disclose the situations related
with the accidents within that cluster. These rules are compared with the rules
created for the EDS and resemblance shows that association rules for EDS does not
disclose correct data that can be related with an accident. If more feature are
presented large information can be identified that is associated with an
accident. To buildup our methodology, we also performed analysis of all clusters
and EDS on monthly or hourly basis. The results of analysis assist methodology
that performing clustering prior to analysis helps to identify better and
useful results that cannot obtained without using cluster analysis.

The paper 3 performsstatistical
and empirical analysis on State Highways and Ordinary District Roads accidental
datasets.The need of the study is to analyze the traffic accident data of SH’s
and ODR’s to assign the black spots and accidental elements, part to control
the harm caused by the accidents. The basic necessity of the analysis is to
check the traffic associated dataset through Exploratory Visualization
Techniques, K-means and KNN Algorithms using Rstudio.. The term accident black
spot in management of road traffic safety defines a place where accidents are
been focus historically and to analyze the accidental data using exploratory
visualization techniques and machine learning algorithms. These techniques and
algorithms are used on the traffic accidental dataset to get the desired output
in order to reduce the accident frequency.  Exploratory Visualization Technique is a
technique to anatomize and examine the sets of data in order to abridge and
encapsulate the important characteristics with visual and pictorial method.
Exploratory Visualization analysis can be performed using scatter plot,
correlation analysis, barplot, clustered barplot, histogram, pie chart etc. Machine
learning concentrates on algorithm designing and makes predictions on sets of
data. It includes Supervised (KNN Algorithm) and Unsupervised learning (K-means
Algorithm).This paper present result by resembling the above  three mining techniques and assigns the cause
of accident, accident prone area, analyze the time of accident, examine the
cause of accident and scrutinize the litigators vehicle.

In paper 4, describes
about a frame work that uses K-mode clustering technique as aprimary task for
dividing 11574 accidents on road network of Dehradun (India) from 2009 to 2014.
Then an association mining rule are used to find out the various context associated
with instance of an accident for both the whole data set and clusters find out by
K-modes clustering algorithm. Then compare the findings from cluster based
analysis and entire data set. The results shows that the amalgamation of k mode
clustering and association mining rule is very encouraging, as it produces
important facts that would remain hidden if no segmentation has been performed
prior to generate association rules. Also a trend analysis has been performed
on each clusters and entire data set. By trendanalysis it shows that before
analysis, prior segmentation of data is very important. This paper put forward
a frame work based on cluster analysis using k-mode algorithm and association
mining rule. By using cluster analysis as a primary task can group the data into
different homogeneous parts. It is the first time that both association and
clustering rule are used together to analyze the data’s for road accidents. The
output of the study proves that by using cluster analysis as a primary task, it
can help in removing heterogeneity to some extent in the road accident data.)
Based on attributes accident type, road type, lightning on road and road
feature, K -modes clustering find six cluster (C1–C6). Association mining rule
have been applied on each cluster as well as on entire data set to generate
rules. For this analysis strong rules with high lift values are used.

The paper 5describes purpose
of data mining methods in the field of road accident investigation. . Association
rules are used to identify the patterns and rules that are subjected the cause
the occurrence of road accidents. An efficient method for updating the index
year after year could be designed. Additionally, further analysis of traffic
safety data using data mining techniques are allowed.Cluster analysis evaluates data objects without consulting a
common class label. The objects are clustered or arranged on the basis of maximizing
the intra class similarity and minimizing the interclass similarity. Outlier
analysis: A database having data objects that do not satisfies the general
behavior or model of the data. These data objects are also called outliers.
Evolution analysis which defines and models consistencies or trends for
objects whose behavior changes over time.We are currently build up by
considering several issues, changes in clash occurrence may have some
aftereffect for traffic safety measures in certain countries. The determination
of specific precautionary measures to overcome clashes requires study of other
factors such as the identification of specific road sections that need work,
etc..It analyzed the traffic accident using data mining technique that could possibly
reduce the fatality rate. Using a road safety database enables to reduce the
fatality by implementing road safety programs at local and national levels.

            The paper 6 describes data mining
techniques to analyze high-frequency accident locations and further identify
different factors that affect road accidents at specifying locations. We first
partitioned the accident locations into k groups based on their accident
frequency poll using k-means clustering algorithm. Association rule mining
algorithm is used to reveal the correlation between different elements in the
accident data and understand the characteristics of these locations. Hence, the
major significance will be the evaluation of the outcomes. Data mining has been proven
as a reliable technique to analyzing road accident data. Several data mining
techniques such as clustering, classification and association rule mining are
widely used in the literature to identify reasons that affect the severity of
road accidents. It is the first time that k-means algorithm is used to identify high- and
low-frequency accident locations based on accident count as it provides some
technical measures to divide the accident locations based on threshold
values.The road accident dataset and its analysis using k-means clustering and association rule mining algorithm
shows that this approach can be reused on other accident data with more
attributes to identify various other factors associated with road accidents.

In paper 7 describesthe
results from analysis of traffic accidents on the Finnish roads by applying
large scale data mining methods. The set of data collected from road traffic
accidents are vast, multidimensional and diverse.The Finnish Road Administration
between 2004 and 2008 data was collected for this study. This set of data
contain more than 83000 accidents and 1203 of which are fatal. The main aim of
this is to examinethe usability of robust clustering, association and frequent
itemsets, and visualization methods to the roadtraffic accident analysis. The
output shows that the pick out data miningmethods are able to produce
intelligible patterns from the data, detectingmore information that could be
increased with more detailed and comprehensive data sets.Most of the fatal
accidents occur due to the condition of single roadway mainroads outside built-up
areas where the permitted speed varies typically between 80-100km/h. Aged and
young drivers have large contribution to the high risk accidents inhighways.
Most of the surveys reported that one of the major reason for accidents among
young people are consumption of alcohol. From the analysis it is understand
that failure of roads and end user groups are responsible for accidents at
certain limit.

            This paper 8 is to represent a Traffic Accident Report
and Analysis System (TARAS) through data mining using Clustering technique. Detect
the causes of accidents is the main aim of this paper. The transport department
of government of India produced the dataset for the study contains traffic
accident records of the yearand look into the performance of J48. The
classification accuracy on the test result discloses the three cases such as
accident, vehicle and casualty. Genetic Algorithms is used for the future
selection to lower the measurements of the dataset..More detailed area specific
information from accident locations and circumstances are needed. With the help
of this paper, the analysis can be done and therefore preventive measures can
be taken. It can help the government to keep track of records of the accidents,
causes of accident, vehicle number, vehicle owner’s name and address.. With the
current data it is possible to identify the risky road segments and the road
user groups responsible for accidents in certain environments. The viewer or
user can also make their own account for viewing the site .you can view the
data about causality .Our system will provide the graphical view of the
accidents with respect to the data entered into the system according to the
period .This system will provide the solutions as accidents causes. So that
with the help of thissystem government can take the necessary actions
accordingaccidents cases.

1) Accurate Location of
accident

2) GPS integration

3) Government ID Authentication
for user Data

4) Advanced Filter
technique Accident Solutionprediction.

The paper9 describes application of data miningtechniques on road accidents by using
machine learning algorithms that determines accident rate in the future to
decrease clash deaths and wounds.The accident dataset contains traffic accident
report of various cities examined by using machine learningalgorithms to
predict the accident rate.It implemented hybrid approach that performed with
higheraccuracy rate as compared to other methods to be described. The machine
learning techniques is used for to reduce accidents and saves life.We have to
expand the classification accuracy of road trafficaccidents types; data quality
has to be added.

In paper 10 describes
about a method called Innovators Marketplace on Data Jackets. Innovators
Marketplace on Data Jackets used to externalize the value of data through ally.
For analyzing the rate of traffic accidents on urban area   methods such as factor analysis, structure
equation modeling and data mining are used here. To construct traffic accident
risk evaluation model different indexes such as total number of accidents
reported, fatality rate injury rate   are
combined. To identify the connection between different factors population
structure information, vehicle information, road characters are used. InHere we
focused on urban data, applied structural equation modeling to find out
theimportant factors associated with traffic accident.  Important  
factors are   population structure,
vehicle information, structure of road etc. This paper describes six factors by
constructing an accident risk causal framework based on urban data and
thecomponent factor sets of each feature and influence on traffic accident.

CONCLUSION

In this paper, we have
collected different researchers works together in one document as analysis and
examined about the contribution towards the effects of road and traffic
accident on human life and society. This survey focused the number of
approaches used to avoid the accident happened in various cities and countries.
The study on road traffic accident is to identify the key element quickly and
efficiently to provide instructional methods to prevent or to reduce the road
traffic accidents. Meanwhile, it would be helpful for improving the efficiency
and security service level of the road transportation system. The paper also
discussing about various data mining techniques which is proved supporting to
resolve traffic accident severity problem and conclude which one could be
optimal technique in road traffic accident scenario.  From our study, we conclude that Association rule
is an important method to analyze road traffic accidents. The brief survey will
also help us to find better mining technique in this kind of problem.

REFERENCE

1 Analysis
of Road Traffic Fatal Accidents Using Data Mining Techniques

Liling Li,
Sharad Shrestha, Gongzhu Hu

2
Analysing road accident data using association
rule mining

Sachin Kumar; DurgaToshniwa

3Black
Spot and Accidental Attributes Identification on State Highways and Ordinary
District Roads Using Data Mining Techniques.

Gagandeep Kaur

4          A data mining framework to analyze
road accident data

           Sachin Kumar, Durga Toshniwal

5          An
overview of data mining in road traffic and accident analysis

          K. Jayasudha Dr. C.
Chandrasekar

6  A data mining approach to
characterize road accident locations

            Sachin KumarEmail author,
Durga Toshniwal

7          Mining road traffic
accidents

           Sami Ayramo,Pasi
Pirtala,Janne Kauttonen,Kashif Naveed,Tommi Karkk ainen

8Traffic Accident Report Analysis using Data
Mining Techniques

Mrs. Kanchan Gawande1 Ambikesh Pandey

9 A Radical Approach
to Forecast the Road AccidentUsing Data Mining Technique

AnupamaMakkar ,Harpreet
Singh Gill

10      Evaluating model of
traffic accident rate on urban data

Jianshi Wang,Yukio Ohsawa

 

 

 

 

 

 

 

 

 

 

 

 

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