TRAFFIC MANAGEMENT USING BIG DATA ANALYTIC TOOL

Authors(4) :-Saranya Krishna , Sharanya K S , Shwetha K S , Dr. Jitendranath Mungara

The analysis of the large-scale data of transportation and accidents has many potentials and it can give very useful insights from the hidden relationship of data. Accidents datasets are used to find main causes of traffic accidents which mainly causes traffic causality and congestion. The vehicular causality dataset used to study human behavior effect on causing traffic accidents. This paper uses nine attributes and two classification algorithm to analyze and predict the possibilities of traffic accidents in python coding environment. Python has a large and comprehensive standard library . pandas is a software library written for the PYTHON programming language for data manipulation and analysis. The random forest algorithm and naïve Bayes algorithm predicts the possibilities of outcomes being a sign of any accident. Checking for the accurate results from algorithms rules and policies are made which is submitted to practioners and decision makers for further road safety measures

Authors and Affiliations

Saranya Krishna
ISE Department, New Horizon College of Engineering, Bengaluru, Karnataka, India
Sharanya K S
ISE Department, New Horizon College of Engineering, Bengaluru, Karnataka, India
Shwetha K S
ISE Department, New Horizon College of Engineering, Bengaluru, Karnataka, India
Dr. Jitendranath Mungara
ISE Department, New Horizon College of Engineering, Bengaluru, Karnataka, India

Big Data, data mining, PYTHON

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Publication Details

Published in : Volume 2 | Issue 3 | May-June 2017
Date of Publication : 2017-06-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 777-781
Manuscript Number : CSEIT1723260
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Saranya Krishna , Sharanya K S , Shwetha K S , Dr. Jitendranath Mungara, "TRAFFIC MANAGEMENT USING BIG DATA ANALYTIC TOOL", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 3, pp.777-781, May-June-2017.
Journal URL : http://ijsrcseit.com/CSEIT1723260

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