Using risk analysis to prioritise road-based intelligent transport systems (ITS) in Queensland

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 incident management services and in particular variable message signs (VMS), which are relatively immature disciplines compared to traditional road engineering. The objective of incident management and VMS is to minimise the safety, efficiency, reliability and environmental impacts of incidents on the operations of the transport system. This may be achieved by informing travellers of the incidents so they can adapt their behaviour in a manner that reduces community impacts, such as lateness and the associated vehicle emissions, unreliability of travel times, as well as secondary accidents due to incidents.\ud \ud \ud \ud Generally, road authorities do carry out needs assessments, but qualitatively in many cases. Therefore, this masters research presents a framework that is systematic, quantitative and relatively easy to implement. In order to prioritise VMS infrastructure deployment, a risk management approach was taken that focuses on minimising the impacts on, and costs to the community. In the framework and case study conducted, safety, efficiency and reliability, and environmental impacts are quantified 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 roads with the highest risk of incident network impacts and therefore the roads with the highest need for intervention. A cost-effectiveness based risk-reduction ranking can then be determined for each incident management treatment type, comparing the net risk with treatment to that without treatment, and dividing by the net present value of deployment. The two types of ranking, pure risk and cost-effectiveness based risk reduction, will help to minimise the network impacts on the community and optimise resource allocation