QUT ePrints

Identifying differences in safe roads and crash prone roads using clustering data mining

Emerson, Daniel, Nayak, Richi, & Weligamage, Justin (2011) Identifying differences in safe roads and crash prone roads using clustering data mining. In Engineering Asset Management 2011: Proceedings of the Sixth Annual World Congress on Engineering Asset Management [Lecture Notes in Mechanical Engineering], Duke Energy Center, Cincinatti, Ohio.

View at publisher

Abstract

Road asset managers are overwhelmed with a high volume of raw data which they need to process and utilise in supporting their decision making. This paper presents a method that processes road-crash data of a whole road network and exposes hidden value inherent in the data by deploying the clustering data mining method. The goal of the method is to partition the road network into a set of groups (classes) based on common data and characterise the class crash types to produce a crash profiles for each cluster. By comparing similar road classes with differing crash types and rates, insight can be gained into these differences that are caused by the particular characteristics of their roads. These differences can be used as evidence in knowledge development and decision support.

Impact and interest:

Citation countsare sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

131 since deposited on 29 Nov 2011
44 in the past twelve months

Full-text downloadsdisplays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 47305
Item Type: Conference Paper
Keywords: data mining, clustering, road crash modelling
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Pattern Recognition and Data Mining (080109)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Simulation and Modelling (080110)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > CIVIL ENGINEERING (090500) > Transport Engineering (090507)
Divisions: Past > Schools > Computer Science
Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2011 WCEAM
Deposited On: 30 Nov 2011 09:10
Last Modified: 01 Sep 2014 19:41

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page