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Road accident reports show that many accidents involve pedestrians, the category of road users generally considered “weak” with respect to other mobility users, and that these accidents occur at pedestrian crossings. Therefore, traffic engineers need simulation tools that can forecast the results of a given design solution and compare the solution with alternatives. A simplified model that simulates interactions between pedestrians and vehicles at road crossings is presented. In the model, the crossing process is represented as a discrete events system, and the model uses basic and easy-to-collect parameters to estimate interactions. Pedestrian behavior in the decision phase is characterized with a gap acceptance criterion, which is based on parameters derived from probabilistic distribution and takes into account the heterogeneity of the pedestrian population. Interaction between pedestrians during the crossing phase is taken into account with a cellular model of the crossing area. The model allows estimation of safety benefits for pedestrians and crossing level of service for both pedestrians and vehicular flows, starting from site geometry and field measurements of flow parameters of pedestrians and vehicles. The model was applied to a real site in a case study. The effects of traffic-calming interventions were also simulated and evaluated.

It is well known that maintenance planning affects, in general, the life of the structures, material wear, and quality of service. In particular, the maintenance of rail tracks affects the traffic volume as well, and therefore it is an important issue for the management of a railway system. Accurate maintenance planning is necessary to optimize resources. The condition of railways is checked by special diagnostic trains. Because of the vast amount of data that these trains record, it is necessary to analyze these data through an appropriate decision support system (DSS). However, the most up-to-date DSSs, such as EcoTrack, are based on a binary logic with rigid thresholds and complicated algorithms with a large number of rules that restrict their flexibility in use. In addition, they adopt considerable simplifications in the rail track deterioration model. In this paper, a neurofuzzy inference engine has been implemented for a DSS to overcome these drawbacks. Based on fuzzy logic, it was able to handle thresholds expressed as a range, an approximate number, or even a verbal value. Moreover, through artificial neural networks, it was possible to obtain more precise rail track deterioration models. The results obtained with the proposed model have been clustered through a fuzzy procedure to optimize the maintenance schedule, thus grouping the interventions in space and in time.