Exploring the determinants of older adults' susceptibility to pedestrians' incidents.

Older pedestrians (65 years and above) are among the most vulnerable road users. As the number and proportion of older adults grows in many countries, and awareness of the benefits of active travel becomes increasingly evident, it behooves researchers to further investigate the safety of older pedestrians. This study contributes to the literature by examining the factors that influence older adults' susceptibility to pedestrian incidents (falls incidents and pedestrian-vehicle collisions). To this end, a sample of older adults living in the Greater Golden Horseshoe, South Ontario, Canada was surveyed. The data were analyzed using descriptive statistics, Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM), to identify several latent factors that are significantly associated with older adults' involvement in pedestrian incidents. The results revealed that the factors that affect older adults' susceptibility to pedestrian incidents are level of difficulty while walking, fear of falling, and crossing evaluation capability. In contrast, level of pedestrian confidence and level of risk-taking crossing behavior are not among the significant factors. It was also found that older pedestrians aged 65-75 years, males, and those who are still working had a greater probability to be involved in pedestrian incidents. These findings shed light on some key constraints of older pedestrians, and the extent to which each factor could improve their safety. This information, in turn, can be used by transportation authorities in prioritizing their plans, policies, and programs towards improving the safety and mobility of older pedestrians.

A systematic assessment of the use of opponent variables, data subsetting and hierarchical specification in two-party crash severity analysis.

Road crashes impose an important burden on health and the economy. Numerous efforts have been undertaken to understand the factors that affect road collisions in general, and the severity of crashes in particular. In this literature several strategies have been proposed to model interactions between parties in a crash, including the use of variables regarding the other party (or parties) in the collision, data subsetting, and estimating models with hierarchical components. Since no systematic assessment has been conducted of the performance of these strategies, they appear to be used in an ad-hoc fashion in the literature. The objective of this paper is to empirically evaluate ways to model party interactions in the context of crashes involving two parties. To this end, a series of models are estimated using data from Canada's National Collision Database. Three levels of crash severity (no injury/injury/fatality) are analyzed using ordered probit models and covariates for the parties in the crash and the conditions of the crash. The models are assessed using predicted shares and classes of outcomes, and the results highlight the importance of considering opponent effects in crash severity analysis. The study also suggests that hierarchical (i.e., multi-level) specifications and subsetting do not necessarily perform better than a relatively simple single-level model with opponent-related factors. The results of this study provide insights regarding the performance of different modelling strategies, and should be informative to researchers in the field of crash severity.