Effects of Signalization at Rural Intersections Considering the Elderly Driving Population

The main objective of this study is to quantify the safety impacts of signalization at Florida’s rural three-leg and four-leg stop-controlled intersections by estimating crash modification factors. The intersections are those in which stop signs are provided for the minor approaches or all-way stop-controlled intersections. The crash modification factors (CMF) are estimated using the cross-sectional method. Generalized linear models (GLM) and multivariate adaptive regression spline models (MARS) are employed with four years of Florida crash data. The K-nearest neighbor (KNN) and K-means clustering algorithms are implemented to identify the comparison sites which are sites having similar characteristics as those of the converted intersections. Furthermore, the quasi-induced exposure method is used to evaluate separately the safety effects of signalization for elderly and non-elderly drivers. According to the results, signalization contributes to an increase in property damage only (PDO) and rear-end crashes. In addition, elderly drivers are more at risk of being involved in such crashes than non-elderly drivers. In particular, at rural four-leg two-way stop-controlled intersections, signalization decreases crash severity, and a greater percentage of the decrease is observed for the elderly drivers than non-elderly especially when the intersection has a high level of major-road average annual daily traffic (AADT) and elderly driver proportion. This study also demonstrates that the MARS model shows a better model fit than the GLM model due to its strength in capturing nonlinear relationships and interaction effects among variables. This study’s findings have implications for both practitioners and researchers.


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  • Accession Number: 01692479
  • Record Type: Publication
  • Report/Paper Numbers: 19-03826
  • Files: TRIS, TRB, ATRI
  • Created Date: Feb 11 2019 11:31AM