Simulation of the Policy Landscape of Transportation Infrastructure Financing Using Agent-Based Modeling

Abstract

The existing deteriorating condition of infrastructure systems in the U.S. needs to be improved by expanding innovative financing options. In this paper, a hybrid agent-based/system dynamics model is created to simulate the micro behaviors of the players (namely, the state Departments of Transportation, private institutional investors, and the general public) in this financing process, and aggregates the effects of these micro behaviors on infrastructure investment at the state and national levels. Results of the simulation model are used in a classification and regression tree meta-model to (1) simulate the landscape of infrastructure financing policies and (2) identify the most significant factors affecting the level of investment in transportation infrastructure. The reliability of the conceptual model, the computer simulation, and the data were verified and validated by subject matter experts from organizations heavily involved in infrastructure financing. The model is further validated through sensitivity analysis and uncertainty propagation analysis. The results of the model include the creation of landscape of policies and the identification significant factors affecting transportation infrastructure financing in the U.S. A scenario analysis is implemented and recommendations, including potential policy paths to close the financing gap, are provided based on the simulation results and the policy landscape.