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Knowledge Discovery in Road Accidents Database Integration of Visual and Automatic Data Mining Methods
Linköping University, Department of Computer and Information Science, GIS. Linköping University, The Institute of Technology.
2008 (English)In: International Journal of Public Information Systems, ISSN 1653-4360, Vol. 1, 59-85 p.Article in journal (Refereed) Published
Abstract [en]

Road accident statistics are collected and used by a large number of users and this can result in a huge volume of data which requires to be explored in order to ascertain the hidden knowledge. Potential knowledge may be hidden because of the accumulation of data, which limits the exploration task for the road safety expert and, hence, reduces the utilization of the database. In order to assist in solving these problems, this paper explores Automatic and Visual Data Mining (VDM) methods. The main purpose is to study VDM methods and their applicability to knowledge discovery in a road accident databases. The basic feature of VDM is to involve the user in the exploration process. VDM uses direct interactive methods to allow the user to obtain an insight into and recognize different patterns in the dataset. In this paper, I apply a range of methods and techniques, including a paradigm for VDM, exploratory data analysis, and clustering methods, such as K-means algorithms, hierarchical agglomerative clustering (HAC), classification trees, and self-organized-maps (SOM). These methods assist in integrating VDM with automatic data mining algorithms. Open source VDM tools offering visualization techniques were used. The first contribution of this paper lies in the area of discovering clusters and different relationships (such as the relationship between socioeconomic indicators and fatalities, traffic risk and population, personal risk and car per capita, etc.) in the road safety database. The methods used were very useful and valuable for detecting clusters of countries that share similar traffic situations. The second contribution was the exploratory data analysis where the user can explore the contents and the structure of the data set at an early stage of the analysis. This is supported by the filtering components of VDM. This assists expert users with a strong background in traffic safety analysis to be able to intimate assumptions and hypotheses concerning future situations. The third contribution involved interactive explorations based on brushing and linking methods; this novel approach assists both the experienced and inexperienced users to detect and recognize interesting patterns in the available database. The results obtained showed that this approach offers a better understanding of the contents of road safety databases, with respect to current statistical techniques and approaches used for analyzing road safety situations.

Place, publisher, year, edition, pages
2008. Vol. 1, 59-85 p.
Keyword
Visual data mining, K-Means, HAC, SOM, InfoVis, IRTAD, GLOBESAFE
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-13189OAI: oai:DiVA.org:liu-13189DiVA: diva2:18004
Available from: 2008-04-28 Created: 2008-04-28 Last updated: 2009-01-26
In thesis
1. Analytical tools and information-sharing methods supporting road safety organizations
Open this publication in new window or tab >>Analytical tools and information-sharing methods supporting road safety organizations
2008 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

A prerequisite for improving road safety are reliable and consistent sources of information about traffic and accidents, which will help assess the prevailing situation and give a good indication of their severity. In many countries there is under-reporting of road accidents, deaths and injuries, no collection of data at all, or low quality of information. Potential knowledge is hidden, due to the large accumulation of traffic and accident data. This limits the investigative tasks of road safety experts and thus decreases the utilization of databases. All these factors can have serious effects on the analysis of the road safety situation, as well as on the results of the analyses.

This dissertation presents a three-tiered conceptual model to support the sharing of road safety–related information and a set of applications and analysis tools. The overall aim of the research is to build and maintain an information-sharing platform, and to construct mechanisms that can support road safety professionals and researchers in their efforts to prevent road accidents. GLOBESAFE is a platform for information sharing among road safety organizations in different countries developed during this research.

Several approaches were used, First, requirement elicitation methods were used to identify the exact requirements of the platform. This helped in developing a conceptual model, a common vocabulary, a set of applications, and various access modes to the system. The implementation of the requirements was based on iterative prototyping. Usability methods were introduced to evaluate the users’ interaction satisfaction with the system and the various tools. Second, a system-thinking approach and a technology acceptance model were used in the study of the Swedish traffic data acquisition system. Finally, visual data mining methods were introduced as a novel approach to discovering hidden knowledge and relationships in road traffic and accident databases. The results from these studies have been reported in several scientific articles.

Place, publisher, year, edition, pages
Linköping: LiU-Tryck, 2008. 118 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1187
Keyword
Visual data mining, STRADA, GLOBESAFE, Conceptual model, System thinking, Internet GIS
National Category
Computer Science
Identifiers
urn:nbn:se:liu:diva-11596 (URN)978-91-7393-887-7 (ISBN)
Public defence
2008-06-12, Alan Turing, Hus E, Campus Valla, Linköpings universitet, Linköping, 13:15 (English)
Opponent
Supervisors
Available from: 2008-04-28 Created: 2008-04-28 Last updated: 2009-04-21Bibliographically approved

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