Using risk analysis to prioritize intelligent transport systems : variable message sign case study in Gold Coast City, Australia
Johnston, Katherine A., Ferreira, Luis, & Bunker, Jonathan M. (2006) Using risk analysis to prioritize intelligent transport systems : variable message sign case study in Gold Coast City, Australia. Transportation Research Record: Journal of the Transportation Research Board, 1959, pp. 28-36.
With perpetual strains on resources, road agencies need to develop network-level decision-making frameworks to ensure optimum resource allocation. This is especially true for intelligent transport systems (ITS) and, in particular, variable message signs (VMSs), a key component of incident management services. The objective for VMSs is to minimize the safety, efficiency, reliability, and environmental impact of incidents on the operations of the transport system. This may be achieved by travelers being informed of the incidents so they can adapt their behavior in a manner that reduces community impact, such as lateness and the associated vehicle emissions, unreliability of travel times, and secondary accidents due to incidents. Generally, road authorities carry out needs assessments, but qualitatively in many cases. Therefore, a framework is presented that is systematic, quantitative, and relatively easy to implement. A risk management approach that focuses on minimizing the impact on and costs to the community was taken to prioritize VMS infrastructure deployment. In the presented framework and case study, safety, efficiency, reliability, and environmental effects are quantified by using an economic risk management approach to determine an overall risk score. This score can be used to rank road sections within the network, indicating the road sections with the highest risk of incident network impact and therefore the road sections with the highest need for intervention. A cost-effectiveness-based risk reduction ranking can then be determined for VMS, with the net risk with treatment being compared with that without treatment, and the net present value of deployment being divided. The two types of ranking, pure risk and cost-effectiveness–based risk reduction, will help to minimize the network impact on the community and optimize resource allocation.
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|Item Type:||Journal Article|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > Schools > School of Urban Development
|Copyright Owner:||Copyright 2006 National Academy of Sciences|
|Copyright Statement:||Reproduced in accordance with the copyright policy of the publisher.|
|Deposited On:||06 Nov 2007 00:00|
|Last Modified:||29 Feb 2012 13:20|
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