IRJET- Road Accident Prediction using Machine Learning Algorithm

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 03 | Mar 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 3494
Road Accident Prediction using Machine Learning Algorithm
Vipul Rana1, Hemant Joshi2, Deepak Parmar3, Pradnya Jadhav4, Monika Kanojiya5
1,2,3,4UG Scholar, Computer Engineering Department, Shah and Anchor Kutchhi Engineering College, Mumbai,
Mumbai University, Maharashtra, India
5Professor, Computer Engineering, Shah and Anchor Kutchhi Engineering College, Mumbai, Mumbai University,
Maharashtra, India
---------------------------------------------------------------------***----------------------------------------------------------------------
Abstract - The traffic has been transformed into the
difficult structure in points of designing and managing by the
reason of increasing number of vehicles. This situation has
discovered road accidents problem, influenced public health
and country economy and done the studies on solution of the
problem. Large calibrated data agglomerations have
increased by the reasons of the technological improvements
and data storage with low cost. Arising the need of accession
to information from this large calibrated data obtained the
corner stone of the data mining. In this study, assignment of
the most compatible machine learning classification
techniques for road accidents estimation by data mining has
been intended.
1. INTRODUCTION
One of the most complicated and difficult daily needs is
overland transportation.
In India, more than 150,000 people are killed each year in
traffic accidents. That’s about 400 fatalities a day and far
higher than developed auto markets like the US, which in
2016 logged about 40,000.Everyyearover1millionvehicles
are added to traffic averagely. 1.2 million People have died
and over 50 million people have been injured in road
accidents in the world every year. Studies on traffic have
executed that road accidents and death- laceration ratiowill
increase.
Design and control of traffic by advanced systems come in
view as the important need. Assumption on the risks in
traffic and the regulations and interventions in the end of
these assumptions will reduce the road accidents. An
assumption system which will be prepared with available
data and new risks will be advantageous.
Data mining concept had been come up with by increasing
and storage of data in the digital stage. Data mining involves
the studies which will discover information from systematic
and purposeful data structures obtained from disordered
and meaningless data.
Machine learning which is sub-branch of artificial
intelligence supplies learning of computer taking advantage
of data warehouses. Assumption abilities of computer
systems have advanced in the event of machine learning.
Utilization of machine learning is a widespread and
functional method for taking authentic decisions by using
experience. Machine learning is able to attain extract
information from data and use statistical method.
1.1 Objectives
The main objective of the road accident prediction system:
 Analyze the previously occurred accidents in the
locality which will help us to determine the most
accident-prone area and help us to set up the
immediate required help for them.
 To make predictions based on constraints like
weather, pollution, road structure, etc.
1.2 Problem Statement
There are several problems with current practices for
prevention of the accidents occurred in the localities. The
database we will use is available officially bymanyinstitutes
and government websites. The data collected will be
analysed, integrated and grouped together based on
different constraints using the best suited algorithm. This
estimation will be helpful to analyse and identify the flaw
and the reasons of the accidents. It will also be helpful while
making roads and bridges as a reference to avoid the same
problems faced before. The predictions made will be very
much useful to plan the management of such problems.
2. Logistic Regression
Logistic regression is the regression analysis and dependent
upon the variables is binary numbers i.e. (0s and 1s), All
regression analysis, the logistic regression is a prediction
analysis. Logistic regression is used todetailsaboutdataand
to graphically explain the relationship between dependent
binary variable and more nominal, ordinal, interval
independent variables.
Sometimes logistic regressions are difficult to describe the
statistics tools are easily conduct and analysis the datasets,
then in others plain word are as it is display in the output.

3. Software and Languages used
3.1 Jupyter
Jupyter exists to develop open-source software. It is used for
open-standards, and services for interactive computing
across dozens of programming languages. It is an open-
source web application that allows you to create and share
documents and code live. Which is a very big advantage of
Jupyter. It can be used for data cleaning and transformation,
numerical simulation, statistical modeling, machinelearning
and much more. We used Jupyter to run the algorithm.
3.2 Python
Python is an interpreted, high-level and a general-purpose
programming language. Created by Guido van Rossum and
first released in 1991, Python has a design philosophy that
emphasizes code readability, notably using significant
whitespace. It is the most used programming language
presently. It provides constructs that enable clear
programming on both small and large scales. The logistic
regression in the system is implemented in jupyter and the
algorithm is written in python language.
3.3 HTML,CSS,JSCRIPT
These are the most commonly used webpage development
languages. The user interface designed for the prediction
system is been developed using theselanguages.Thewebsite
acts as an interface which takes the input those are the
various constraints from theusersandpassestotheprogram
to work upon.
4. WORKING
The working of the project is divided in to parts.
4.1 Data Set Selection
Data is the most import part when you work on prediction
systems. It plays a very vital role your whole project i.e., you
system depends on that data. So selection of data is the first
and the critical step which should be performed properly,
For our project we got the data from the government
website. These datasets were available for all. There are
other tons of websites who provide such data. The dataset
we choose wad selected based on the various factors and
constraints we were going to take under the consideration
for our prediction system.
4.2 Data Cleaning and Data Transformation
After we have selected the dataset. The next step is to clean
the data and transform it into the desired format as it is
possible the dataset we use may be of different format. It is
also possible that we may use multiple datasets from
different sources which may be indifferentfileformats.Soto
use them we need to convert them into the format we want
to or the type that type prediction system supports. The
reason behind this step is that it is possible that the data set
contains the constraints which are not needed by the
prediction system and including them makes the system
complicated and may extend the processing time. Another
reason behind data cleaning is the dataset may contain null
value and garbage values too. So the solution to this issue is
when the data is transformed the garbage values are
replaced. There are many methods to perform that.
4.3 Data Processing and Algorithm Implementation
After the data is been cleaned and transformed it’s ready to
process further. After the data has beencleanedand wehave
taken the required constraints. We divide the whole dataset
int o the two parts that can be either 70-30 or 80-20. The
larger portion of the data is fortheprocessing.Thealgorithm
is applied on that part of data. Which helps the algorithm to
learn on its own and make prediction for the future data or
the unknown data. The algorithm is executed in which we
take only the required constraintsfromthecleaneddata.The
output of the algorithm is in ‘yes’ and ‘no’. It gives the error
rate and the success rate.
4.4 Output and User Side Experience
After the prediction system is ready to use. The Website is
developed for the user. The user just has to fill a form which
consists of different options theyneedtoselect.Theyarelike
the type of climate, the type of vehicle and so on. Once the
user submits the form the algorithm is triggered and the
input given by the user is passed to the prediction system.
The user is given how accident prone the road can be in
percentage.
5. RESULTS
In the road accident prediction project use the dataset is in
terms of values and some data is plain English word so, the
numerical values data is easily predicted and also the
calculation are easily done but, the normal word are display
as it is or the non predicted data are drop in the table.

So, This dataset are many columnsandrowsandall numbers
of null values will be fulfill in forward fill method and also
use the classification algorithm entire dataset.
In that classification algorithm we will use Logistic
Regression Algorithm The logistic algorithm will make the
prediction in terms of percentage, to find accuracy level in
percentage and Error percentages.ThisAlgorithmisonlyfor
the yes and no type of result or successful and unsuccessful.
The equation for combinations of all 15 input variables.
The classification algorithm of theentiredataset.IntheRoad
Accident prediction final result is to find the percentage of
accident in particular area. Having lower numberoffeatures
helps the algorithm to converge faster and increases
accuracy.
In the Road Accident prediction final result is to find the
percentage of accident in particular area. Then we apply
logistic regression on these features and obtain the least
error.
6. CONCLUSION
Road Accidents are caused by various factors. By going
through all the research papers it can be concluded that
Road Accident cases are hugely affected by the factors such
as types of vehicles, age of the driver, age of the vehicle,
weather condition, road structure and so on. Thus we have
build an application which gives efficient prediction of road
accidents based on the above mentioned factors.
REFERENCES
[1] Peden, M. (2004) "World report on road traffic injury
prevention". Geneva: World Health Organization
[2] M. Chang, L. Y., & Chen, W. C. (2005). "Data mining
of tree- based models to analyse freeway accident
frequency". Journal of Safety Research, 36(4), 365-375
[3] Tesema, T. B., Abraham, A., & Grosan, C. (2005). "Rule
mining and classification of road traffic accidents using
adaptive regression trees". International Journal of
Simulation, 6(10), 80-94.
[4] Maze, T. H., Agarwai, M., & Burchett,G.(2006)."Whether
weather matters to traffic demand, traffic safety, and
traffic operations and flow".

IRJET- Road Accident Prediction using Machine Learning Algorithm

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