International journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN:2313-626X

Frequency: 12
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 Volume 5, Issue 8 (August 2018), Pages: 113-121

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 Original Research Paper

 Title: Density-based clustering for road accident data analysis

 Author(s): Abdullah S. Alotaibi *

 Affiliation(s):

 Computer Science Department, Shaqra University, Shaqra, Saudi Arabia

 https://doi.org/10.21833/ijaas.2018.08.014

 Full Text - PDF          XML

 Abstract:

Now days, road accidents due to traffic are increasingly being recognized as key issue for transportation agencies as well as common people. A considerable unexpected output of transportation systems is road accidents with injuries and loss of lives. In order to suggest safe driving, precise study of road traffic data is serious to discover elements that are related to mortal accidents. In this research paper, we discover factors behind road traffic accidents problem solving by data mining algorithms together with DBSCAN and Parallel Frequent mining algorithm. We initially divide the accident places into k clusters depends on their accident frequency with DBSCAN algorithm. Next, parallel frequent mining algorithm is apply on these clusters to disclose the association between dissimilar attributes in the traffic accident data for realize the features of these places and analyzing in advance them to spot different factors that affect the road accidents in different locations. The main objective of accident data is to recognize the key issues in the area of road safety. The efficiency of prevention accidents based on consistency of the composed and predictable road accident data using with appropriate methods. Road accident dataset is used and implementation is carried by using Weka tool. The outcomes expose that the combination of DBSCAN and parallel frequent mining explores the accidents data with patterns and expect future attitude and efficient accord to be taken to decrease accidents. 

 © 2018 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Accident analysis, DBSCAN, Road accident dataset, FP growth, Weka

 Article History: Received 9 April 2018, Received in revised form 11 June 2018, Accepted 13 June 2018

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2018.08.014

 Citation:

 Alotaibi AS (2018). Density-based clustering for road accident data analysis. International Journal of Advanced and Applied Sciences, 5(8): 113-121

 Permanent Link:

 http://www.science-gate.com/IJAAS/2018/V5I8/Alotaibi.html

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