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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.

In our fast-growing world, we need to create increasingly efficient systems to ensure further growth and sustainability. This also applies to transportation, where a key limitation is the bottle-necks of road network capacity. To eliminate, or at least, to moderate these bottlenecks, they must first be localised. In this case study, a model is proposed to objectively identify the weak points of the road infrastructure in the Western Hungarian region, a typical part of the Hungarian road net-work, based on automated data input. This way, there is no need to visually analyse the road net-work on site, but it is possible to evaluate the available information and suggest efficient measures from the distance. The model is suitable for general application, meaning it can serve other regions or countries as well, and enables macro-level decision-makers to take steps to eliminate those weak points. A fuzzy signature rule base is applied by the authors, which systematically maps and models the various attributes of the road network. The model currently contains more than 20 independent variables as inputs, but they can be easily expanded or replaced if further inputs need to be included.

The process of traffic control systems significantly relies on the immediate detection of breakdown states. As a result of their crisp (non-fuzzy) based calculation procedures, conventional traffic estimators and predictors cannot effectively model traffic states. In fact, these methods are characterized by exact features, while traffic is defined by uncertain variables with vague properties. Furthermore, typical numerical methodologies have constraints on evaluating the overall system status in heterogeneous and convoluted networks mainly due to the absence of reliable and real-time data. This study develops a fuzzy inference system that uses data from the Hungarian freeway networks for predicting the severity of congestion in this complex network. Congestion severity is considered the output variable, and traffic flow along with the length and the number of lanes of each section are assigned as input variables. Seventy-five fuzzy production rules were generated using accessible datasets, percentile distribution, and experts' consensus. The MATLAB fuzzy logic toolbox simulates the designed model and analysis steps. According to available resources, the results demonstrate linkages among input variables. Analyses are also used to construct intelligent traffic modeling systems and further service-related planning.

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