Strategic Capital Investment Analytics: An Agent Based Approach to California High-Speed Rail Ridership Model

Abstract

In this paper, we present an agent-based model (ABM) of multi-dimensional transportation choices for individuals and firms given anticipated aggregate traveler demand patterns. Conventional finance, economic and policy evaluation techniques have already been widely adopted to more evidenced based decision-making process with the aim to understand the financial, economic and social impacts on transportation choices. Prior scholars have examined common practices used to measure profitability for investment appraisal including internal rate of return (IRR), net present value (NPV) and risk analysis approaches, incorporating the concepts of time value of money and uncertainty to assess potential financial gains with different transportation projects. However, using conventional capital budget planning or static scenario analysis alone cannot capture significant, interactive and nonlinear project, demand and market uncertainties. Here we build an agent-based model on the current California High-Speed Rail (HSR) to provide insights into firm investment decisions from a computational finance perspective, given the coupling of individual choices, aggregate social demand, and government policy and tax incentives. Given individual level choice and behavioral aspects, we combine financial accounting and economic theory to identify more precise marginal revenue streams and project profitability over time to help mitigate both project and potential, system market risk.