The statistics analysis and Fatal Accident Reporting System

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
at building 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) to solve 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
technique is a framework that is used as an initial work for division of
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.

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The paper 3 performs statistical
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.

Traffic Accident
Report Analysis using Data Mining Techniques

Mrs.
Kanchan Gawande1 Ambikesh Pandey

            This
paper 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
year and 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 this system government can take
the necessary actions according accidents cases.

1) Accurate Location of
accident

2) GPS integration

3) Government ID
Authentication for user Data

4) Advanced Filter
technique Accident Solution prediction