Modelling uncertainty in the sustainability of Intelligent Transport Systems for highways using probabilistic data fusion

The implementation of ITS to increase the efficiency of saturated highways has become increasingly prevalent. It is a high level objective for many international governments and operators that highways should be managed in a way that is both sustainable i.e. environmental, social and economically sound and supportive of a Low-Carbon-Energy Future. Some clarity is therefore needed to understand how Intelligent Transport Systems perform within the constraints of that objective. The paper describes the development of performance criteria that reflect the contributions of Information Communication Technology (ICT) emissions, vehicle emissions and the embedded carbon within the physical transport infrastructure that typically comprises one type of Intelligent Transport System i.e. Active Traffic Management - a scheme that is used to reduce inter-urban congestion. The performance criteria form part of a new framework methodology 'EnvFUSION' (Environmental Fusion for ITS) outlined here. This is illustrated using a case study where environmental performance and pollution baselines (collected from independent experts, academic, governmental sources and suppliers) are processed using an attributional Lifecycle Assessment tool. The tool assesses the production and operational processes of the physical infrastructure of Active Traffic Management using inputs from the 'Ecoinvent' database. The ICT component (responsible for data links) is assessed using direct observation, whilst vehicle emissions are estimated using data from a National Atmospheric Emissions Laboratory. Analytical Hierarchy Process and Dempster-Shafer theory are used to create a prioritised performance hierarchy: the Intelligent Transport Sustainability Index, which includes weighted criteria based on stakeholder expertise. A synthesis of the individual criteria is then used to reflect the overall performance of the Active Traffic Management scheme in terms of sustainability (low-carbon-energy and socio-economic) objectives. Robust modelling approach in terms of relevance towards modelling sustainability of intelligent transport.ITS performance data collected from multiple sources and aggregated via D-S theory.Benefits include integrating transport, ICT and operational (energy) variables in a sustainability index.Attributional LCA indicates a high degree of CO2 within the infrastructure.Sustainability performance indicates priority should focus on improving data center energy consumption.