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