Developing a macroscopic model based on fuzzy cognitive map for road traffic flow simulation

Fuzzy cognitive maps (FCM) have been broadly employed to analyze complex and decidedly uncertain systems in modeling, forecasting, decision making, etc. Road traffic flow is also notoriously known as a highly nonlinear system. Even though applications of FCM risk analysis presented various engineering fields, this research aims at modeling road based on macroscopic characteristics through FCM. Therefore, simulation variables involved with carried out reasoning historical data collected from the e-toll dataset Hungarian networks freeways. The proposed model developed 58 selected freeway segments “concepts” FCM; moreover, new inference rule for employing process along its algorithms presented. results illustrate representation computation real their main traffic-related that reached an equilibrium point. Furthermore, by performing customized scenarios presented, which objectives such predicting future state, route guidance scenarios, geometric indication, effectual mobility can be evaluated.