U & I aware: A framework using data mining and collision detection to increase awareness for intersection users
Salim, Flora, Loke, Seng Wai, Rakotonirainy, Andry, & Krishnaswamy, Shonali (2007) U & I aware: A framework using data mining and collision detection to increase awareness for intersection users. In 21st International Conference on Advanced Information Networking and Applications Workshopes, 21-23 May 2007, Niagara Falls, Canada.
An intersection safety system should adapt to the particular characteristics that identify an intersection, by mining traffic and collision data. Given the large amount of sensor data that are obtained for intersections and from sensor-equipped cars, analysis and learning of such data is essential. This paper presents a new method to improve safety at intersections using a combination of a mathematical based collision detection algorithm and data mining. A number of scenarios at a simulated intersection are explored with encouraging results from our data mining implementation. The results suggest that our approach can help improve situation awareness and automate understanding of intersections, which, in turn, can be used to increase safety at intersections.
Impact and interest:
Citation countsare sourced monthly fromand 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 theindexing service can be viewed at the linked Google Scholar™ search.
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.
|Item Type:||Conference Paper|
|Keywords:||data mining, sensor data|
|Subjects:||Australian and New Zealand Standard Research Classification > COMMERCE MANAGEMENT TOURISM AND SERVICES (150000) > TRANSPORTATION AND FREIGHT SERVICES (150700) > Road Transportation and Freight Services (150703)|
Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400)
Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > PURE MATHEMATICS (010100) > Ordinary Differential Equations Difference Equations and Dynamical Systems (010109)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Simulation and Modelling (080110)
|Divisions:||Current > Research Centres > Centre for Accident Research & Road Safety - Qld (CARRS-Q)|
Current > QUT Faculties and Divisions > Faculty of Health
Current > Institutes > Institute of Health and Biomedical Innovation
|Copyright Owner:||Copyright 2007 IEEE|
|Copyright Statement:||Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
|Deposited On:||05 Dec 2007|
|Last Modified:||29 Feb 2012 23:34|
Repository Staff Only: item control page