Applications of Artificial Intelligence Paradigms to Decision Support in Real-Time Traffic Management

Abstract

Decision support for real-time traffic management is a critical component for the success of intelligent transportation systems. Theoretically, microscopic simulation models can be used to evaluate traffic management strategies in real time before a course of action is recommended. However, the problem is that the strategies would have to be evaluated in real time; this might not be computationally feasible for large-scale networks and complex simulation models. To address this problem, two artificial intelligence (AI) paradigms—support vector regression (SVR) and case-based reasoning (CBR)—are presented as alternatives to the simulation models as a decision support tool. Specifically, prototype SVR and CBR decision support tools are developed and used to evaluate the likely impacts of implementing diversion strategies in response to incidents on a highway network in Anderson, South Carolina. The performances of the two prototypes are then evaluated by a comparison of their predictions of traffic conditio...