Spatial analysis of road crashes involving vulnerable road users in support of road safety management strategies

Road traffic crashes result in the deaths of approximately 1.3 million people around the world each year and leave between 20 and 50 million people with non-fatal injuries. Due to insufficient physical protection in the event of a collision, more than half of all road traffic deaths are among vulnerable road users (VRU), i.e., pedestrians, cyclists, and motorcyclists. The urban road environment poses a high risk to VRU. However, traffic crashes can be reduced by using appropriate safety countermeasures on “hazardous road locations”(HRL) where higher collision frequencies are observed with respect to the average expected level. It is important to highlight that evaluating each sub-category of VRU separately plays a key role to find the most effective countermeasures for each specific VRU. This study presents the analyses and results about the spatial distribution of traffic collisions to identify HRL in Turin from 2006 to 2019 by considering all VRU and related sub-categories (pedestrians, cyclists, moped, and motorcycle users respectively). The Italian National Institute of Statistics (ISTAT) provided the official database of traffic collisions. Firstly, the crash data relating to regional (Piedmont), provincial (Turin), and municipal (Turin) levels was evaluated by using descriptive statistics. The crash data of Turin was then prepared and organized to carry out a detailed analysis. Due to the absence of a complete geographic coordinates in the crash database, data was geo-localized firstly and then analyzed with the help of Geographic Information System (GIS) technologies. The distance-based and density-based methods were used for the spatial distribution analyses of the traffic collisions. While distance-based methods (Nearest Neighbor Analysis, G and F Functions) evaluate distances between events to define areas where traffic crashes are clustered, density-based methods (the Kernel Density Estimation) were used to examine the crash density to identify HRL. The crash database was extracted as six-time intervals by dividing it into 2–3-year periods to highlight the presence of false-positive and false-negative HRL. All critical road segments and intersections which presented 3 out of 6 positive time intervals in the road network were identified as HRL. These analyses were carried out for all VRU and the related VRU sub-categories. The results indicate collisions were concentrated in the main intersections of the city, which deal with heavy traffic flows and conflicts between users during the day. It is a clear fact that wide cross-sections in the urban road environment cause some difficulties to VRU due to significant speed differences with respect to motorized users, the absence of signalized junctions, and protected pedestrian crossings in some points. It seems that most hazardous road locations(HRL) are for specific sub-categories rather than others. So, safety countermeasures should be differentiated based on the specific VRU sub-category to be protected.