The number of elderly people is increasing worldwide, especially in Europe. Such an aging of the population has numerous consequences for society, many of which relate to transportation: older people, aware of their reduced abilities, prefer walking to driving. This leads to an increase in the elderly walking population and thus the need to understand and address the safety issues of these road users. Although these issues are well known, this topic has been little researched so far. The objective of this research is to provide a deeper insight into the safety level of elderly pedestrians by recognizing repetitive patterns leading to accidents involving them, to highlight the magnitude of the problem by analyzing a 10-year pedestrian crash database, to develop a model predicting—on the basis of the recognized patterns—the severity level of collisions involving older pedestrians, and, finally, on the basis of the highlighted factors, to propose some countermeasures to improve their safety. In order to achieve this goal, first, a statistical analysis of the database is performed, considering 13 factors that lead to accidents. Second, Kolmogorov–Smirnov and Anderson–Darling tests are performed to check if the data follow a normal distribution. Finally, an ordinal logistic regression model is proposed to determine the relationship between the crash severity level and the factors characterizing collisions. Thanks to this model, the statistical influencing factors are highlighted. Finally, based on the previous analysis, some technical and educational countermeasures are proposed.

Even though the European roads are among the safest in the world, the number of road accidents is still a cause for concern. To reduce their number and consequences, many studies are being conducted, including knowledge of the factors that influence the occurrence of accidents. Forensic traffic experts are also part of the treatment of traffic accidents, and they often must base their conclusions on proven incomplete studies of data collected by police officers. In some cases, traffic accident data are still collected in classical ways and with classical measuring equipment. This is often a source of error. This paper defines these errors and offers solutions that are shown primarily through data capture using 3D scanners and photogrammetry. In this way, we can perfectly recreate the situation in the event of a traffic accident through 3D models, thus eliminating many shortcomings of police drawings and records. The article also proposes a central database of traffic accidents as an additional solution to gain a deeper insight into the causes and consequences of traffic accidents.