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Analysis of Roadway Traffic using Data Mining Techniques : A Review

Authors(2):

Nisha A. Solanke, Prof A. D. Gotmare
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Roadway traffic safety is a major concern for transportation governing agencies as well as ordinary citizens. Data Mining is taking out of hidden patterns from huge database. It is commonly used in a marketing, surveillance, fraud detection and scientific discovery. In data mining, machine learning is mainly focused as research which is automatically learnt to recognize complex patterns and make intelligent decisions based on data. Globalization has affected many countries. There has been a drastic increase in the economic activities and consumption level, leading to expansion of travel and transportation. The increase in the vehicles, traffic lead to road accidents. Considering the importance of the road safety, government is trying to identify the causes of road accidents to reduce the accidents level. The exponential increase in the accidents data is making it difficult to analyze the constraints causing the road accidents. The paper describes how to mine frequent patterns causing road accidents from collected data set. We find associations among road accidents and predict the type of accidents for existing as well as for new roads. We make use of association and classification rules to discover the patterns between road accidents and as well as predict road accidents for new roads.

Nisha A. Solanke, Prof A. D. Gotmare

Data Mining, Association Rule, Classification Rule, Apriori Algorithm, Naïve Bayes Algorithm

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Publication Details

Published in : Volume 4 | Issue 1 | January-February - 2018
Date of Publication Print ISSN Online ISSN
2018-01-31 2395-1990 2394-4099
Page(s) Manuscript Number   Publisher
44-48 IJSRSET184128   Technoscience Academy

Cite This Article

Nisha A. Solanke, Prof A. D. Gotmare, "Analysis of Roadway Traffic using Data Mining Techniques : A Review", International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 4, Issue 1, pp.44-48, January-February-2018.
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