Bayesian Poisson Modeling of Built Environment Effects on Pedestrian Crash Risk Among Older Adults in Mountainous Urban Areas

In the context of rapid population aging in China, ensuring pedestrian safety for older adults has become a critical concern, particularly in mountainous cities where the built environment’s role remains understudied. This study examines how built environment factors influence road traffic crashes involving older pedestrians in such terrains, aiming to propose targeted safety optimization strategies. Using ten-year road traffic crash data from Yuzhong District, Chongqing, the research employed both Standard Poisson Regression and Bayesian Poisson Regression models for analysis. Key findings indicate that crash frequency significantly increased with higher densities of footbridges and recreational facilities, as well as with a greater proportion of parks and green space, whereas it decreased with a higher land use mix, greater densities of educational facilities, and higher public transport stop density. The proportion of storage land and the density of medical facilities showed no significant effects. These results provide concrete, evidence-based guidance for urban planning and transportation management in mountainous cities to optimize pedestrian infrastructure and enhance walking safety for the elderly.