Detection of Interesting Traffic Accident Patterns by Association Rule Mining
In recent years, the accident rate related to traffic is high. Analyzing the crash data and extracting useful information from it can help in taking respective measures to decrease this rate or prevent the crash from happening. Related research has been done in the past which involved proposing vari...
Main Author: | |
---|---|
Other Authors: | |
Format: | Others |
Language: | en |
Published: |
LSU
2013
|
Subjects: | |
Online Access: | http://etd.lsu.edu/docs/available/etd-07012013-160705/ |
id |
ndltd-LSU-oai-etd.lsu.edu-etd-07012013-160705 |
---|---|
record_format |
oai_dc |
spelling |
ndltd-LSU-oai-etd.lsu.edu-etd-07012013-1607052013-07-04T03:14:08Z Detection of Interesting Traffic Accident Patterns by Association Rule Mining Donepudi, Harisha Electrical & Computer Engineering In recent years, the accident rate related to traffic is high. Analyzing the crash data and extracting useful information from it can help in taking respective measures to decrease this rate or prevent the crash from happening. Related research has been done in the past which involved proposing various measures and algorithms to obtain interesting crash patterns from the crash records. The main problem is that large numbers of patterns were produced and vast number of these patterns would be obvious or not interesting. A deeper analysis of the data is required in order to get the interesting patterns. In order to overcome this situation, we have proposed a new approach to detect the most associated sequential patterns in the crash data. We also make use of the technique, Association Rule Mining to mine interesting traffic accident patterns from the crash records. The main goal of this research is to detect the most associated sequential patterns (MASP) and mine patterns within the data sets generated by MASP using a modified FP-growth approach in regular association rule mining. We have designed and implemented data structures for efficient implementation of algorithms. The results extracted can be further queried for pattern analysis to get a deeper understanding. Efficient memory management is one of the main objectives during the implementation of the algorithms. Linked list based tree structures have been used for searching the patterns. The results obtained seemed to be very promising and the detected MASPs contained most of the attributes which gave a deeper insight into the crash data and the patterns were found to be very interesting. A prototype application is developed in C# .NET. Soysal, Omer Zhang, Jian Chen, Jianhua LSU 2013-07-03 text application/pdf http://etd.lsu.edu/docs/available/etd-07012013-160705/ http://etd.lsu.edu/docs/available/etd-07012013-160705/ en unrestricted I hereby certify that, if appropriate, I have obtained and attached herein a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to LSU or its agents the non-exclusive license to archive and make accessible, under the conditions specified below and in appropriate University policies, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. |
collection |
NDLTD |
language |
en |
format |
Others
|
sources |
NDLTD |
topic |
Electrical & Computer Engineering |
spellingShingle |
Electrical & Computer Engineering Donepudi, Harisha Detection of Interesting Traffic Accident Patterns by Association Rule Mining |
description |
In recent years, the accident rate related to traffic is high. Analyzing the crash data and extracting useful information from it can help in taking respective measures to decrease this rate or prevent the crash from happening. Related research has been done in the past which involved proposing various measures and algorithms to obtain interesting crash patterns from the crash records. The main problem is that large numbers of patterns were produced and vast number of these patterns would be obvious or not interesting. A deeper analysis of the data is required in order to get the interesting patterns. In order to overcome this situation, we have proposed a new approach to detect the most associated sequential patterns in the crash data. We also make use of the technique, Association Rule Mining to mine interesting traffic accident patterns from the crash records. The main goal of this research is to detect the most associated sequential patterns (MASP) and mine patterns within the data sets generated by MASP using a modified FP-growth approach in regular association rule mining. We have designed and implemented data structures for efficient implementation of algorithms. The results extracted can be further queried for pattern analysis to get a deeper understanding. Efficient memory management is one of the main objectives during the implementation of the algorithms. Linked list based tree structures have been used for searching the patterns. The results obtained seemed to be very promising and the detected MASPs contained most of the attributes which gave a deeper insight into the crash data and the patterns were found to be very interesting. A prototype application is developed in C# .NET. |
author2 |
Soysal, Omer |
author_facet |
Soysal, Omer Donepudi, Harisha |
author |
Donepudi, Harisha |
author_sort |
Donepudi, Harisha |
title |
Detection of Interesting Traffic Accident Patterns by Association Rule Mining |
title_short |
Detection of Interesting Traffic Accident Patterns by Association Rule Mining |
title_full |
Detection of Interesting Traffic Accident Patterns by Association Rule Mining |
title_fullStr |
Detection of Interesting Traffic Accident Patterns by Association Rule Mining |
title_full_unstemmed |
Detection of Interesting Traffic Accident Patterns by Association Rule Mining |
title_sort |
detection of interesting traffic accident patterns by association rule mining |
publisher |
LSU |
publishDate |
2013 |
url |
http://etd.lsu.edu/docs/available/etd-07012013-160705/ |
work_keys_str_mv |
AT donepudiharisha detectionofinterestingtrafficaccidentpatternsbyassociationrulemining |
_version_ |
1716590496066830336 |