A Macroscopic Forecasting Framework for Estimating Socioeconomic and Environmental Performance of Intelligent Transport Highways

The anticipated introduction of new forms of intelligent transport systems (ITS) represents a significant opportunity for increased cooperative mobility and sociotechnical changes within the transport system. Although such technologies are currently technically feasible, various socioeconomic and environmental barriers impede their arrival. This paper uses a recently developed ITS performance assessment framework, i.e., Environmental Fusion (EnvFUSION), to perform dynamic forecasting of the performance for three key ITS technologies: active traffic management (ATM), intelligent speed adaptation (ISA), and an automated highway system (AHS) using a mathematical theory of evidence. A consequential lifecycle assessment (c-LCA) is undertaken, which forms part of a data fusion process using data from various sources. The models forecast improvements for the three ITS technologies in line with social acceptability, economic profitability, and major carbon reduction scenarios up to 2050 on one of the U.K.'s most congested highways. An analytical hierarchy process (AHP) and the Dempster-Shafer theory (DST) are used to weight criteria that form part of an intelligent transport sustainability index (ITSI). Overall performance is then synthesized. Results indicate that there will be a substantial increase in socioeconomic and emissions benefits, provided that the policies are in place and targets are reached, which would otherwise delay their realization.