Roads are a strategic asset of a country and are of great importance for the movement of passengers and goods. Increasing traffic volume and load, together with the aging of roads, creates various types of anomalies on the road surface. This work proposes a low-cost system for real-time screening of road pavement conditions. Acceleration signals provided by on-car sensors are processed in the time–frequency domain in order to extract information about the condition of the road surface. More specifically, a short-time Fourier transform is used, and significant features, such as the coefficient of variation and the entropy computed over the energy of segments of the signal, are exploited to distinguish between well-localized pavement distresses caused by potholes and manhole covers and spread distress due to fatigue cracking and rutting. The extracted features are fed to supervised machine learning classifiers in order to distinguish the pavement distresses. System performance is assessed using real data, collected by sensors located on the car’s dashboard and floorboard and manually labeled. The experimental results show that the proposed system is effective at detecting the presence and the type of distress with high classification rates.

Nowadays, cold recycling is considered as a common road construction/maintenance technique and no longer as an alternative technique to the traditional ones. The difficulties in simulating the production and construction processes in laboratory have led researchers to practise full-scale testing of mixtures. This paper presents a comprehensive evaluation of the mechanical performance of a cement–bitumen-treated materials (CBTMs) containing 80% of reclaimed asphalt. After a preliminary mix design, the mixture was produced in a mix plant and laid down on two trial sections with different layer thicknesses (15 and 20 cm) over a homogeneous subgrade. The mixture was sampled before laying, compacted and characterised through indirect tensile test and indirect tensile stiffness modulus test. The same tests were carried out on cores taken from the trial sections. Light weight deflectometer and falling weight deflectometer surveys were performed on the trial sections to evaluate the performances of the CBTMs.

Road safety is still a major issue all around the world. The capability to analyze the road network and identify high risk sections is crucial in road safety management. Therefore, it is essential for road administrations, practitioners, and researcher to have a clear and practical framework of the available road network safety analysis procedures. The aim of this study is to provide such a framework by carrying out an exhaustive analysis of the main procedures available all around the world. The proposed literature review has started considering a web search on Web of Science (WoS). Then, a systematic review of each publication has been carried out using the Bibliometrix software, to identify the main characteristics of the publications within the specific topic. Then, the most relevant and widespread safety analysis procedures have been considered and the following aspects have been analyzed: the type of approach (crash analysis, crash prediction models procedures, based on road safety inspections, etc.), which and how many data are required (crashes, traffic, visual inspections, geometrical data, etc.), which is the effectiveness of the procedure, and which are the segmentation criteria used (fixed length, variable length based on geometry, traffic, etc.). Ten different procedures for road network safety analysis have been considered for detailed analysis. The research findings highlight that each procedure has its own pros and cons. The choice of the best procedure to use is highly related to the characteristics of the road network that need to be analyzed, to the availability of data, and to the main elements the Road Authorities (RA) wants to give priority to. This collection and review of different procedures will be of great interest for RAs, practitioners, and researchers in the process of selecting the most useful procedure to use to carry out a road network safety analysis.

The PRACT Project (Predicting Road ACcidents – a Transferable methodology across Europe) is an ongoing project aimed at developing a European accident prediction model structure that could, with proper calibration, be applied to different European road networks.The core principles behind the PRACT project structure are that:•it is unrealistic to think that one unique Accident Prediction Model (APM) model with a unique set of Crash Modification Factors (CMFs) can actually be developed, valid for all Europe and for all the different type of networks;•the development of a specific APM model and a set of CMFs based on local data is extremely time consuming and expensive and requires data and experience that most road administrations do not have;•the development of “local” CMFs only based on historical local data prevents the possibility of evaluating the effectiveness of new technologies.The PRACT project addresses these issues by developing a practical guideline and a user friendly tool that will allow for an easier implementation of APMs and CMFs in different countries and on different road networks