Traffic safety and energy efficiency of vehicles are strictly related to driver’s behavior. The scientific literature has investigated on some specific dynamic parameters that, among the others, can be used as a measure of unsafe or aggressive driving style such as longitudinal and lateral acceleration of vehicle. Moreover, the use of modern mobile devices (smartphones and tablets), and their internal sensors (GPS receivers, three-axes accelerometers), allows road users to receive real time information and feedback that can be useful to increase awareness of drivers and promote safety. This paper focuses on the development of a prototype mobile application that can evaluate the grade of safety that drivers are keeping on the road by measuring of accelerations (longitudinal and lateral) and warning for users when it can be convenient to correct their driving style. The aggressiveness is evaluated by plotting vehicle’s acceleration on a g-g diagram specially studied and designed, where horizontal and lateral acceleration is displayed inside areas of “Good Driving Style”. Several experimental tests were carried out with different drivers and cars in order to estimate the system accuracy and the usability of the application. This work is part of the wider research project M2M, Mobile to Mobility: Information and communication technology systems for road traffic safety (PON National Operational Program for Research and Competitiveness 2007-2013) which is based on the use of mobile sensor computing systems for giving real-time information in order to reduce risks and to make the transportation system more safe and comfortable.

The European Union policy for road safety management is based on the European Directive 2019/1936/EC. Among the safety management procedures and strategies, road safety inspections (RSI) are an effective tool for preventing accident risk and reducing crash frequency and severity for existing road networks. The European Transport Safety Council encourages the extension of these measures to the main urban and rural roads by the 5th Road Safety Action Programme. In light of the above, in this study, a safety performance evaluation through the RSI approach is carried out for a high-risk rural road in Southern Italy in order to identify all the road infrastructure-related features with poor safety conditions. Afterwards, the relationship between infrastructure deficiencies and the frequency and severity of accidents is investigated; a significant relationship between accident density/total number of injured people and road markings gap is found. Furthermore, the results confirm that a high density of driveways strongly impacts crash frequency. The analysis of the contribution of multiple infrastructure-related variables on the crash occurrences is proposed by the identification of several mathematical models. A second-order AIC (Akaike’s information criterion) approach is carried out to compare the five fitted models investigated. Finally, a prediction calibrated model is proposed.

Steelmaking industries produce a large amount of solid wastes that need to be adequately managed in order to ensure environmental sustainability and reduce the impact of their disposal on earth pollution. Electric arc furnace (EAF) slags are those wastes deriving from secondary steelmaking production; these slags can be re-used and recycled in many industrial applications such as the production of asphalt mixes. In this paper authors investigate the surface performance of EAF slags used as second-hand aggregate in asphalt mixes. In particular, slags behavior under polishing is compared to other types of aggregate commonly used for asphalt concrete such as limestone, basalt, and kinginzite. Several devices (skid tester, laser profilometer, polishing machine) were used to collect experimental data; the analysis of microtexture was carried out by comparing aggregate surface performance before and after polishing. Results show that EAF slags are positively comparable to basalt as concerns the polishing behavior; good correlations between skid resistance and some microtexture indicators are also carried out.

Microsimulation models are widespread for the analysis of roundabouts operational performance providing realistic modelling of vehicle movements. These models are based on many independent parameters to describe traffic and driver behaviour, which need to be calibrated in order to better match field data. In practice, despite the well-recognized importance of calibration and validation processes, simulation is conducted under default values because of difficulties in field data collection and deficiency in available guidelines. These issues can be faced by using transferability methodologies that allow applying the parameters calibrated for a case study to other similar locations. Therefore, this paper investigates the suitability of the transferability procedure adopting both the application-based and estimation-based approaches, by considering two roundabouts and a microsimulation tool. A Genetic Algorithm technique was used to determine the best estimates of these model parameters. After that, the authors compared field-measured with simulated queue lengths, considering four different scenarios. The results show that the application of Wiedemann 99 parameters calibrated for the first case study to the second one allows reducing the RMSNE more than 50%, thus confirming an acceptable level of transferability of these parameters between the two case studies.

Horizontal curves are identified as high-severity crash locations along different types of roads: driver deceleration and acceleration rates when approaching and departing horizontal curves play a key role in developing speed profiles. Drivers' behavior along curves is strictly related to the perception of the road geometry that, in turn, affects road safety. This article addresses an in depth-study on drivers' behavior along horizontal curves by monitoring vehicles speed through mobile devices equipped with a prototype mobile application. A sample of 35 participants was selected; overall 86 curves with different radius were investigated. Participants driving behavior was analyzed by deriving lateral accelerations from global positioning system (GPS) data. To assess how driving experience can affect the perception of the road geometry, expert drivers were involved in the experiments. Results show that the accumulated driving experience allows to adequately calibrate speed choices when approaching unsafe curves, having a better road geometry perception. Conversely, some drivers tend to overestimate their driving abilities; this lack of safety cognition may lead to erroneous speed estimations and to an unsafe driving behavior. Finally, this research shows that mobile devices (if adequately equipped and managed) may be used to enhance the awareness of drivers and promote traffic safety.