Post incident traffic management on a freeway network depends mainly on the decisions of traffic managers that surface from their individual exposure to similar conditions. This manual approach to tackle a dynamic scenario induces uncertainty, inconsistency and inefficient use of rescue resources. This paper suggests a decision support scheme known as Freeway Incident Analysis System, (FIAS). The novel idea presented here is the use of real time data from the Toll Collection System (TCS) and Vehicle Detection System (VDS). This data is conjugated with the historical data on a microscopic simulation platform to predict traffic flows in a post-incident scenario. The system employs Cellular Automata for the microscopic simulation of vehicle movement, and we also suggest two additional rules in the modified version of the conventional model. This has enabled us to model the dynamic flow parameters in a post incident scenario more realistically. The evaluation of FIAS indicates that it yields significantly accurate post incident information about traffic flows for the use of traffic managers.