Scenarios discovery : robust transportation policy analysis in Singapore using microscopic traffic simulator

Abstract

One of the main challenges of making strategic decisions in transportation is that we always face a set of possible future states due to deep uncertainty in traffic demand. This thesis focuses on exploring the application of model-based decision support techniques which characterize a set of future states that represent the vulnerabilities of the proposed policy. Vulnerabilities here are interpreted as states of the world where the proposed policy fails its performance goal or deviates significantly from the optimum policy due to deep uncertainty in the future. Based on existing literature and data mining techniques, a computational model-based approach known as scenario discovery is described and applied in an empirical problem. We investigated the application of this new approach in a case study based on a proposed transit policy implemented in Marina Bay district of Singapore. Our results showed that the scenario discovery approach performs well in finding the combinations of uncertain input variables that will result in policy failure.