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Two main elements are essential in terms of road traffic safety. The first element is accident prevention and the second is the minimization of accident severity once a crash has occurred. Concrete safety barriers have very good anti-collision performance against roadside obstacles, relatively modest construction and maintenance costs, and low dynamic deflection and therefore are widely used in tunnels. Thanks to their characteristic redirective profile these barriers can redirect errant vehicles into their original lane after collisions. However insufficient research has been done for increasing the performance of concrete barriers purposely designed for tunnel installations. This research presents the new “NDBA Tunnel” concrete safety barrier designed and constructed by the Italian Road Operator ANAS indicated to be installed in road tunnel sections for safety improvements. In Europe, road safety barriers must be designed in compliance with the European Standard EN 1317. Therefore, the barrier “NDBA Tunnel” was subject to the TB11 and TB81 full-scale crash tests according to the European EN 1317 regulation. The results prove the barrier's ability to absorb impact loads of light and heavy vehicles with a working width W2. Therefore, the NDBA concrete barrier can be installed on existing or new tunnels at a distance less than or equal to 70 cm from the facing of the tunnel wall.

This research put forward an efficacious real-time deep learning-based technique to automate the process of monitoring the social distancing in transportation systems (e.g., bus stops, railway stations, airport terminals, etc.) and other public spaces with the purpose to mitigate the impact of coronavirus pandemic. The proposed technique makes use of the YOLOv3 model to segregate humans from the background of each image of a surveillance video and the linear Kalman filter for tracking the humans’ motion even in case in which another object or person overlaps the trajectory of the person under analysis. The performance of the model in human detection is extremely high as demonstrated by the accuracy of the model that reaches values higher than 95%. The detection algorithm can be applied for alerting people to keep a safe distance from each other when they are in crowded places or in groups.

Several European road operators and authorities joined the C-Roads Platform with the aim of harmonising the deployment activities of cooperative intelligent transport systems (C-ITS). C-ITS research is preliminary to future automated-driving vehicles. The current conventional highways were designed on traditional criteria and models specifically developed for traffic flows of manually guided vehicles. Thus, this article describes some new criteria for designing and monitoring road infrastructures on the basis of performance features of autonomous (or self-driving) vehicles.

The estimation of traffic flow variables (flow, space mean speed and density) plays a fundamental role in highways planning and designing, as well as in traffic control strategies. Moving Observer Method (MOM) allows traffic surveys in a road, or in a road network. This paper proposes a novel automated procedure, called MOM-AP based on Moving Observer Method and Digital Image Processing (DIP) Technique able to automatically detect (without human observers) and calculate flow q, space mean speed vs and density k in case of stationary and homogeneous traffic conditions.

Nowadays safety in railways is mostly achieved by automated system technologies such as ERTMS/ETCS. Nevertheless, on local railways (suburban and regional lines) several tasks still depend on the choices and actions of a human crew. With the aim to improve safety in such type of railways, this research proposes a system for the automatic detection and recognition of railway signs by means of the digital image processing technique. First field applications, carried out on the Italian railway network, show that the proposed system is very accurate (the percentage of correctly detected railway signs is about 97%), even at high train speeds.