Macro-level road network evaluation by fuzzy signature rule bases

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.