Rule mining and classification of road traffic accidents using adaptive regression trees
- Authors
- Tesema, T.B.; Abraham, A.; Grosan, C.
- Issue Date
- 1-Sep-2005
- Publisher
- UK Simulation Society
- Citation
- International Journal of Simulation: Systems, Science and Technology, v.6, no.10-11, pp 80 - 94
- Pages
- 15
- Journal Title
- International Journal of Simulation: Systems, Science and Technology
- Volume
- 6
- Number
- 10-11
- Start Page
- 80
- End Page
- 94
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/47094
- ISSN
- 1473-8031
1473-8031
- Abstract
- Road traffic accidents are among the top leading causes of deaths and injuries of various levels. Ethiopia is experiencing highest rate of such accidents resulting in fatalities and various levels of injuries. Addis Ababa, the capital city of Ethiopia, takes the lion’s share of the risk having higher number of vehicles and traffic and the cost of these fatalities and injuries has a great impact on the socio-economic development of a society. This research is focused on developing adaptive regression trees to build a decision support system to handle road traffic accident analysis for Addis Ababa city traffic office. The study focused on injury severity levels resulting from an accident using real data obtained from the Addis Ababa traffic office. Empirical results show that the developed models could classify accidents within reasonable accuracy. © 2005 UK Simulation Society. All rights reserved.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - College of Software > School of Computer Science and Engineering > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/47094)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.