Modeling the effects of AADT on predicting multiple-vehicle crashes at urban and suburban signalized intersections.
Accid Anal Prev
; 91: 72-83, 2016 Jun.
Article
en En
| MEDLINE
| ID: mdl-26974024
Annual Average Daily Traffic (AADT) is often considered as a main covariate for predicting crash frequencies at urban and suburban intersections. A linear functional form is typically assumed for the Safety Performance Function (SPF) to describe the relationship between the natural logarithm of expected crash frequency and covariates derived from AADTs. Such a linearity assumption has been questioned by many researchers. This study applies Generalized Additive Models (GAMs) and Piecewise Linear Negative Binomial (PLNB) regression models to fit intersection crash data. Various covariates derived from minor-and major-approach AADTs are considered. Three different dependent variables are modeled, which are total multiple-vehicle crashes, rear-end crashes, and angle crashes. The modeling results suggest that a nonlinear functional form may be more appropriate. Also, the results show that it is important to take into consideration the joint safety effects of multiple covariates. Additionally, it is found that the ratio of minor to major-approach AADT has a varying impact on intersection safety and deserves further investigations.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Conducción de Automóvil
/
Población Suburbana
/
Población Urbana
/
Accidentes de Tránsito
/
Modelos Teóricos
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Aspecto:
Determinantes_sociais_saude
Límite:
Humans
Idioma:
En
Revista:
Accid Anal Prev
Año:
2016
Tipo del documento:
Article
Pais de publicación:
Reino Unido