Citation

Chen Y, Yuan R, Wei J, Li S. Int. Rev. Spatial Plann. Sustain. Devel. 2023; 11(1): 44-63.

Copyright

(Copyright © 2023, International Community of Spatial Planning and Sustainable Development)

DOI

10.14246/irspsd.11.1_44

PMID

unavailable

Abstract

The main travel mode of the elderly group is walking, but with the physical inconvenience caused by the growth of the elderly group, the reaction speed of the elderly decreases, and they are more vulnerable to serious trauma and even death in traffic accidents. In recent years, the modeling of elderly pedestrian traffic accidents from macro aspects is one of the research directions of road traffic safety. The influencing factors of accidents are related to the complex built environment. Considering the spatial heterogeneity at the macro scale, thesis quantitatively analyzes the impact of urban environmental factors on the number of elderly pedestrian traffic accidents in the administrative Street area In this paper, the influence of urban environmental factors on the number of traffic accidents of elderly pedestrians in administrative street areas is quantitatively analyzed at the macro scale considering spatial heterogeneity, and the influencing factor model of the number of traffic accidents of elderly pedestrians considering the macro-built environment is constructed. Examining the spatial autocorrelation of elderly pedestrian crashes based on the Moran index for administrative street divisions. The co-point and co-edge regression matrix was constructed as the spatial weight matrix, and the co-linear variables were determined by variance inflation factor test and stepwise regression. The goodness of fit was compared based on ordinary least squares model and geographically weighted regression model, and the influencing factors of spatial heterogeneity were analyzed. The results show that the accidents are greatly affected by the regional average speed, the average distance between intersections, the density of elderly population, the density of road network and the density of service facilities, and show different performances with the distribution of geographical space.

Language: en

Keywords

Built Environment; Elderly Pedestrian; Geographically Weighted Regression; Traffic Accidents