Intelligent Transportation Management Applying Expert Systems

This paper proposes an expert system whose components are soft computing for modeling, evolutionary algorithms for optimization and granular computing and artificial intelligence for predicting the behavior of system. The aim of this Expert System is to manage an urban network by minimizing the congestion and harmonizing the level of service. Combinatorial optimization algorithms, as subsystem of this decision support system (DSS) are used to solve some traffic problems into an intelligent framework. Such system which may be called ATMS (Advanced Traffic Management System) applies a scenario-based scheme to learn critical situations and propose the reasonable decisions for traffic operator. Some components of this scenario-based system is ramp metering, lane management, variable speed limit, traveler guidance, electronic pay-toll and intersection control. Since it is possible to save experiments in ATMS, the expertness of the system can be improved to do the best for controlling the network by policies with respect to end-users requirements.