###### Abstract  
A driver's behavior and their psychophysical state are the most common causes of road accidents. The research presented in the paper proposes a method that allows the identification of highly dangerous road stretches/intersections in advance, based on the localization of stressful/relaxing situations measured on drivers. These were measured through the collection of physiological parameters using wearable devices. A correlation between stressful/relaxing situations and locations with high accident rates, based on a historical statistical database (black spots), was investigated. A series of driving tests was conducted in the city of Milan. The first set was mostly oriented to the research and validation of the parameters related to the driver's psychophysical state. Subsequent tests allowed the definition of a correlation between black spots and relaxing/stressful areas. The results showed that the most stressful areas for drivers fell mainly within those with high accident rates. Furthermore, 80% of the most dangerous zones of the route were identified using this method, thus confirming the validity of the approach as a support tool for a priori preventive analysis for road safety. The wearable devices allowed the study and the integration of specific elements relating to human behavior in the field of road safety, which typically involves a technical-engineering approach. Keywords:wearable devices; heart rate variability (HRV); galvanic skin response (GSR); accidents; road safety; driving test; driver behavior; risk +
Footnote †: journal: Applied Soft  
Additionally, they are the main cause of death for young people aged between 15 and 29. The United Nations (UN) General Assembly, in 2010, established the Decade of Action for Road Safety (2011-2020), with the goal of halving the number of deaths on roads by 2020, compared to the 2011 records. From the data analysis on accidents, it is possible to appreciate how, currently, the trend of reduction in the number of victims of accidents, even if relevant, is not sufficient to achieve the mentioned goal [1; 2; 3]. From the engineering perspective, two kinds of analysis are usually performed to understand, and therefore, limit the phenomenon of accidents: preventive analysis according to road safety review (RSR) techniques, i.e., the identification of problems concerning the safety of a road infrastructure before the occurrence of an accidental event, and the analysis of historical data on accidents, based on the collection and subsequent processing of accident-related data detected by the different collectors [3]. The two kinds of analysis mentioned, and particularly the analysis of historical data on accidents, are often affected by the following limitations:  
* Unavailability or lack of accuracy of information about the location of accidents. Both the localization and the repetitiveness of accidents in a site are essential elements for the execution of accurate safety analysis;
* Unavailability of data related to car accidents that did not require the intervention of law enforcement agencies or collectors;
* Limited consideration of the human factor in the analyses. This last element is particularly significant, since, although the dynamics of road accidents are the result of a complex interaction between the vehicle, the driver, and the external environment, the responsibility for the accident is, in most cases, attributable to the driver's behavior. Considering the Italian national context, the data collected by the Italian National Institute for Statistics (ISTAT), updated in 2016, show that the circumstances underlying road accidents with injuries to people are mainly due to distracted driving (19.6% of total accidents on extra-urban roads), followed by failure to comply with safety rules (17.4% of the total considering only urban roads), and by right of way and high speed (15.3% of accidents on extra-urban roads) [2].