Users of road facilities generally express a judgment on road quality based on their psychophysical conditions, in relation to the environment they refer to. This judgment is made considering many aspects, for example, the presence of traffic lights, the frequency of interchanges, of lay‐bys and gas stations, route conformation, environmental conditions, quality of road signs, etc. In this article, we propose a new index called Global Satisfaction Index, which uses vehicular traffic quality and quality of road pavement, to summarize these aspects, and to express the users’ global judgment about the ride comfort on rural roads. Since in this kind of judgment a subjective perception process is involved, we have used fuzzy theory to handle uncertainty embedded in the process. The attributes of the aspects considered have been expressed through fuzzy numbers, and the global judgment has been obtained through a fuzzy inference system. In this way the proposed index overcomes the limits of other existing indices, since it incorporates uncertainties and/or imprecision inherent in the drivers’ perception of the ride comfort. Moreover, it can be used for evaluation and comparison of different types of road sections. Finally, a numerical example is presented to assist in understanding the practical aspects of the proposed index.

A new choice model for corrective road maintenance work based on an economic evaluation of users’ expectations and perceptions about road quality is proposed. Because uncertainty affects human subjective perception processes, the model uses fuzzy sets to deal with this kind of uncertainty. Accordingly, the international roughness index was first fuzzified by combining users’ perceptions about pavement conditions with ranges of speeds for four different categories of rural roads. These fuzzy values were then used to calculate vehicle operating costs and freeflow speed. The latter can be considered a function of the international roughness index and the law enforcement factor. To calculate this factor, a fuzzy inference system was set up. Vehicle operating costs and free-flow speed, as well as the value of time, were then used to calculate the travel cost perceived by users. The model finds, through fuzzy maximization of the difference between perceived travel costs before and after interventions, the best allocation of the available budget for a rural road network. In other words, the results suggest the extent of interventions on specific road sections. An application to a real network shows how the proposed model may be used.