EVALUVATING
ROAD TRAFFIC ACCIDENTS USING DATA MINING TECHNOLOGY

 

 

ABSTRACT:-

Road traffic safety is an important perturbation for
government transport authorities as well as common people. Road accidents are
ambivalent and not able to be predict the   incidents.
And their 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.

     
I.           
INTRODUCTION:-

In
modern life, accidents have become daily happening. Every
day we hear the news of the accident 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.

 

 

In
paper 4 , describes about a frame work that uses  K-mode 
clustering technique as a primary 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 trend analysis 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.

 

In
paper7 describers the 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 examine the usability of robust clustering, association and
frequent item sets, and visualization methods to the road traffic accident
analysis. The output shows that the pick out  data mining methods are able to produce  intelligible patterns from the data,
detecting

more
 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  main

roads
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
in

highways.
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.

 

In
paper10 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. In

Here we focused
on urban data, applied structural equation modeling to find out the

important 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 the

component factor
sets of each feature and influence on traffic accident.