Abstract

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.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.