DocumentCode
2618353
Title
Towards a user-centred road safety management method based on road traffic simulation
Author
Gregoriades, Andreas
Author_Institution
Univ. of Cyprus, Nicosia
fYear
2007
fDate
9-12 Dec. 2007
Firstpage
1905
Lastpage
1914
Abstract
One of the most important gaps in road safety management practises is the lack of mature methods for estimating reliability. Road safety performance assessment systems have been developed; however, these provide only historical or retrospective analyses. Effective safety management requires a prospective viewpoint. The main goal of this research is to assist in reducing accident rates in Cyprus by providing ample time to the authorities to react to high risk situations through a safety prediction early warning system. This ultimately will prevent accidents from occurring which subsequently could save lives. Traditional approaches focuses solidly on empirical data concerning road network dynamic properties, despite the fact that the most vulnerable component of the system is the human element. This paper described the integration of agent-based simulation with Bayesian Belief Networks (BBN) for improved quantification of accident probability. The BBN is developed using multidisciplinary influences.
Keywords
accident prevention; belief networks; multi-agent systems; road safety; road traffic; Bayesian belief networks; accident prevention; agent-based simulation; reliability estimatin; road network dynamic properties; road safety performance assessment systems; road traffic simulation; safety prediction early warning system; user-centred road safety management; Bayesian methods; Computational modeling; Humans; Predictive models; Regression analysis; Road accidents; Road safety; Telecommunication traffic; Traffic control; Vehicle dynamics;
fLanguage
English
Publisher
ieee
Conference_Titel
Simulation Conference, 2007 Winter
Conference_Location
Washington, DC
Print_ISBN
978-1-4244-1306-5
Electronic_ISBN
978-1-4244-1306-5
Type
conf
DOI
10.1109/WSC.2007.4419818
Filename
4419818
Link To Document