Mixed Effects Logistic Model to Address Demographics and Neighborhood Environment on Pedestrian Injury Severity
Document Type
Article
Publication Date
2017
Digital Object Identifier (DOI)
https://doi.org/10.3141/2659-19
Abstract
This paper examines the effects of demographics and neighborhood environment on pedestrian injury severity to inform proactive countermeasures for improving pedestrian safety. A mixed effects logistic model addressing unobserved heterogeneity was developed from 3,948 pedestrian-involved crashes that occurred in Florida from 2011 to 2014. Six normally distributed random parameters were identified to reflect random effects on the pedestrian injury severity. The heterogeneity of two demographic factors (older and male pedestrians) suggested the need for more customized education programs to improve pedestrian safety awareness and knowledge, especially for older pedestrians. Relative to low-income areas, 67.7% of pedestrians involved in crashes in higher-income areas were less likely to sustain severe injury. Analysis of sample data also indicated that low-income areas tended to have had more unsafe behaviors by pedestrians related to higher injury severity (e.g., crossing at dark in unlighted areas). Higher-income areas tended to have had more unsafe behaviors by drivers related to higher injury severity (e.g., distracted driving). Other significant factors included lighting conditions (daylight, darkness without lighting), speed limit, alcohol or drug impairment, dart or dash behavior, crossing indicator, and traffic control device indicator. Regarding neighborhood land use types, two indicators about the presence of bus stops and department stores or supermarkets nearby were significant, and their effects were also random. Further investigations are needed to identify systematically the need for effective countermeasures in severe injury crash clusters in the future.
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
Transportation Research Record: Journal of the Transportation Research Board, v. 2659, p. 174-181
Scholar Commons Citation
Guo, Rui; Xin, Chunfu; Lin, Pei-Sung; and Kourtellis, Achilleas, "Mixed Effects Logistic Model to Address Demographics and Neighborhood Environment on Pedestrian Injury Severity" (2017). CUTR Faculty Journal Publications. 90.
https://digitalcommons.usf.edu/cutr_facpub/90
