Car driving is considered a very complex activity, consisting of different concomitant tasks and subtasks, thus it is crucial to understand the impact of different factors, such as road complexity, traffic, dashboard devices, and external events on the driver’s behavior and performance. For this reason, in particular situations the cognitive demand experienced by the driver could be very high, inducing an excessive experienced mental workload and consequently an increasing of error commission probability. In this regard, it has been demonstrated that human error is the main cause of the 57% of road accidents and a contributing factor in most of them. In this study, 20 young subjects have been involved in a real driving experiment, performed under different traffic conditions (rush hour and not) and along different road types (main and secondary streets). Moreover, during the driving tasks different specific events, in particular a pedestrian crossing the road and a car entering the traffic flow just ahead of the experimental subject, have been acted. A Workload Index based on the Electroencephalographic (EEG), i.e., brain activity, of the drivers has been employed to investigate the impact of the different factors on the driver’s workload. Eye-Tracking (ET) technology and subjective measures have also been employed in order to have a comprehensive overview of the driver’s perceived workload and to investigate the different insights obtainable from the employed methodologies. The employment of such EEG-based Workload index confirmed the significant impact of both traffic and road types on the drivers’ behavior (increasing their workload), with the advantage of being under real settings. Also, it allowed to highlight the increased workload related to external events while driving, in particular with a significant effect during those situations when the traffic was low. Finally, the comparison between methodologies revealed the higher sensitivity of neurophysiological measures with respect to ET and subjective ones. In conclusion, such an EEG-based Workload index would allow to assess objectively the mental workload experienced by the driver, standing out as a powerful tool for research aimed to investigate drivers’ behavior and providing additional and complementary insights with respect to traditional methodologies employed within road safety research.

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Asphalt concrete (AC) recycling is probably the most cost-effective technique for the rehabilitation of stressed road pavements and for the construction of new ones. The increased interest in this technology comes from the need of reducing the costs connected to the production processes and to the use of virgin raw materials. In fact, the benefits connected to the use of Reclaimed Asphalt Pavement (RAP) are related to the possibility of substituting the natural aggregates and the virgin binder of an AC mixture, without negatively affecting its mechanical properties. When this process is made at ambient temperature (Cold Recycled Mixes-CRM), more advantages are brought about with, above all, the reduction in energy consumption and emissions during in plant production and laying, in addition to the actual possibility of achieving durable pavement layers. A CRM totally made of RAP is proposed in this research, the main goal of which was to evaluate the different physical and mechanical characteristics derived by the large use of recycled materials. According to the final mix-design, CRM does not show significant differences in terms of physical properties, when compared to a Hot Mix Asphalt (HMA) for base layers. Moreover, even if the experimental mixture *Manuscript Click here to view linked References

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