In recent years, the growing survey capability for continuous and frequent inspections of road pavement conditions has raised questions regarding the optimal solutions for data management. Considering traditional approaches, classical Pavement Management Systems seem inadequate for exploiting the potential of similar data, especially for visualization and infrastructure modelling. The development of BIM procedures may be helpful although the existing solutions are not ready for handling road condition and maintenance management. Furthermore, the recent international regulations gradually adopt BIM in all infrastructure projects, as reference model. Then, as a preliminary approach, in this paper, a solution for presenting and elaborating pavement condition information in an I-BIM environment is proposed. This procedure simplifies representation and elaboration of survey information regarding structural and functional conditions of pavements in a realistic 3D environment, exploiting smart object potential. Specific routines have been coded for upgrading and elaborating specific extended attributes related to pavement condition. This approach represents an important evolution compared to traditional methodologies, as a modern and ‘user-friendly’ decision support tool for road agencies, combining in a single environment all the life cycle phases of the infrastructure. The proposed approach has been tested, in a preliminary form, on an existing motorway, with positive results.

Road pavements are subject to various forms of degradation compromising their functionality with negative effects on safety. For assuring the highest quality, all the distresses have to be properly identified and quantified by road administrators. For increasing efficiency and reducing costs and times of surveys, several innovative methods to detect, classify and measure surface distresses were proposed, with variable results. In this context, the authors propose an algorithm for automated pothole detection through the processing of 3D data of pavement surfaces, acquired using an innovative high-performance equipment. The algorithm, derived from computer vision, is able of identifying potholes in road sections, assuring a reliable estimation of shape and severity, in terms not only of area, perimeter, but also depth, with practical benefits. The numerical results show the remarkable performance of the proposed algorithm, even compared to alternative traditional methodologies. In terms of Precision, Recall and F-Score, it assures mean values equal respectively to 89.75%, 92.95% 91.28%. Validation was also performed in terms of area error rate, with an average value of 5.15%, significantly lower than other approaches. Then, the algorithm represents a reliable alternative to traditional approaches and allows road administrators to derive data to optimize maintenance and road functionality.

Driving behaviour is a direct consequence of the stimuli received from the road infrastructure, from the surrounding environment and from the atmosphere inside the vehicle. Almost all of these perceptions, which affect steering, are received through the drivers? eyes. For this reason, in this paper visual behaviour was examined in order to deduce new indexes connected to mental workload. To this aim, a consistent sample of drivers covered a rural road inside a vehicle, while their eye movements and driving activities were being recorded with suitable instruments. The quantification of some variables involved in the trials permitted the evaluation of visual behaviour and determination of a measure of mental workload, identifying also any situations where performance was compromised. Some reports between mental workload and road geometry, summarized in a few tables, could represent a further aid for road designers and managers.

Recently, even the road industry has been involved in an evolutionary process, inspired by the novel ‘Industry 4.0’. This transition moves the attention towards big-data and Internet of Things concepts, determining novel data flows and relevant management efforts. In smart roads, modern instrumented vehicles and networks of sensors will provide frequent helpful measures (traffic, weather, condition, accidents, mechanical performance, etc.), with reduced efforts and costs. However, this process is currently at the preliminary phases and several issues raise. Since the management and elaboration of huge amounts of data represent a relevant novel issue, in this study, an original web platform for collection and analysis of road performance data is proposed. This platform can acquire and process several data classes and support maintenance activity planning. This paper focuses on pavement maintenance, to provide a reliable decision support tool for road agencies, alternatively feedable by modern survey equipment and, in future, widespread sensors (when effective smart road sensors are installed on the main highways). The platform has been tested, in a preliminary form, on an existing motorway, considering high-performance survey systems data, with interesting and positive results.

The presence of numerous sensors inside modern vehicles leads to the development of new driving assistance tools, the real usefulness of which depends, however, on the environmental context. This study proposes a procedure capable of quantifying the effectiveness of some warnings produced by an On-Board Unit (OBU) inside the vehicle in a specific environmental context, even if limited only to the considered road. The experimentation was carried out by means of a driving simulator with a sample of young users with sufficiently homogeneous characteristics. The collected data were treated by ANOVA to highlight any differentiation between a traditional driving condition, without any instrumental support, and another involving the OBU was present. The results showed that only in relation to the investigated road, the OBU ensured the advantage of sending information of interest to the driver without invalidating their performance in terms of longitudinal and transverse acceleration, speeding, and steering angle. This research could be of interest to the infrastructure managers who, in case of inappropriate use of a road, could intensify active and passive safety devices for users’ safety.