Summary: | This thesis describes the development of accident prediction models for signalized
intersections in the Greater Vancouver Regional District (GVRD). The traffic and road-related
factors which appeared to underlie the occurrence of accidents are examined and
models which explain, in a statistical sense, the generation of accidents as a function of
these factors are developed. Recognizing the statistical and practical shortcomings
associated with the use of the Conventional Linear Regression approach to develop
accident prediction models, it was decided to utilize the Generalized Linear Regression
Models (GLIM) approach. This approach addresses and overcomes the error structure
problems that are associated with the conventional linear regression theory and allows for
the use of nonlinear relationships in the model. In addition, the safety predictions obtained
from the GLIM models can be refined using the Empirical Bayes' approach to provide,
more accurate, site-specific safety estimates. The use of the complementary Empirical
Bayes approach can significantly reduce the regression to the mean bias that are inherent in
observed accident counts.
The study made use of sample accident, traffic and intersection design data corresponding
to signalized intersections located in the Greater Vancouver Region. The accident data set
contained 67 urban intersections from the City of Richmond and 72 urban intersections
from the City of Vancouver giving a total of 139 intersections. Three different types of
models were developed: (1) models relating the total number of accidents to traffic volume; (2) models relating accidents of a specific type to traffic volume; and (3) models
incorporating other geometric design variables such as the existence of left turn lanes,
right turn lanes, pedestrian crossings, etc. The goodness of fit of the models was
evaluated using two statistics: the Scaled Deviance (SD) and the Pearson x2 statistics.
The overall fit of the models was adequate. Three applications of the GLIM models and
the Empirical Bayes refinement process were described. The first related to the
identification of accident prone locations. The second related to the before and after
safety analysis and the third to safety planning. The usefulness of the GLIM model
estimates in accounting for the randomness inherent in the accident occurrence process
and the regression to the mean bias was documented and discussed. === Applied Science, Faculty of === Civil Engineering, Department of === Graduate
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