Vol. 5 No. 2 (2021)
Articles

Machine Learning in a Tunnel Project Simulation

Published 2021-08-14

Abstract

This case study simulation demonstrates that the Melbourne Metro project has about 42% of the benefits felt in terms of public transport user gains, followed by road user gains linked to decongestion (21.5%), and the assets’ residual value, as well as externalities (accounting for about 9.5% of the project benefits). From an economic perspective, gains are about 27.5% of the total. While the composition for both options assumes a similar path, the Melbourne Metro project’s economic and financial benefits outweigh those of the extended program. To reap optimally, therefore, the former option is worth following. Finally, a discount rate of 4% seems to produce better financial returns than when 7% is used as the discount rate, implying that the need to adopt the former value in the project development and operation could not be overstated. Thus, the efficacy of employing the selected algorithms in financial analysis is confirmed.