Year

2017

Season

Spring

Paper Type

Master's Thesis

College

College of Computing, Engineering & Construction

Degree Name

Master of Science in Civil Engineering (MSCE)

Department

Engineering

NACO controlled Corporate Body

University of North Florida. School of Engineering

First Advisor

Dr. Thobias Sando

Second Advisor

Dr. Donna Mohr

Third Advisor

Dr. Don Resio

Fourth Advisor

Dr. O. Patrick Kreidl

Department Chair

Dr. Murat Tiryakioglu

College Dean

Dr. Mark A. Tumeo

Abstract

Pedestrian countdown signals (PCSs) are viable traffic control devices that assist pedestrians in crossing intersections safely. Despite the fact that PCSs are meant for pedestrians, they also have an impact on drivers’ behavior at intersections. This study focuses on the evaluation of the safety effectiveness of PCSs to drivers in the cities of Jacksonville and Gainesville, Florida. The study employs two Bayesian approaches, before-and-after empirical Bayes (EB) and full Bayes (FB) with a comparison group, to quantify the safety impacts of PCSs to drivers. Specifically, crash modification factors (CMFs), which are estimated using the aforementioned two methods, were used to evaluate the safety effects of PCSs to drivers. Apart from establishing CMFs, crash modification functions (CMFunctions) were also developed to observe the relationship between CMFs and traffic volume.

The CMFs were established for distinctive categories of crashes based on crash type (rear-end and angle collisions) and severity level (total, fatal and injury (FI), and property damage only (PDO) collisions). The CMFs findings, using the EB approach indicated that installing PCSs result in a significant improvement of driver’s safety, at a 95% confidence interval (CI), by a 8.8% reduction in total crashes, a 8.0% reduction in rear-end crashes, and a 7.1% reduction in PDO crashes. In addition, FI crashes and angle crashes were observed to be reduced by 4.8%, whereas a 4.6% reduction in angle crashes was observed. In the case of the FB approach, PCSs were observed to be effective and significant, at a 95% Bayesian credible interval (BCI), for a total (Mean = 0.894, 95% BCI (0.828, 0.911)), PDO (Mean = 0.908, 95% BCI (0.838, 0.953)), and rear-end (Mean = 0.920, 95% BCI (0.842, 0.942)) crashes. The results of two crash categories such as FI (Mean = 0.957, 95% BCI (0.886, 1. 020)) and angle (Mean = 0.969, 95% BCI (0.931, 1.022)) crashes are less than one but are not significant at the 95 % BCI.

Also, discussed in this study are the CMFunctions, showing the relationship between the developed CMFs and total entering traffic volume, obtained by combining the total traffic on the major and the minor approaches. In addition, the CMFunctions developed using the FB indicated the relationship between the estimated CMFs with the post-treatment year. The CMFunctions developed in this study clearly show that the treatment effectiveness varies considerably with post-treatment time and traffic volume. Moreover, using the FB methodology, the results suggest the treatment effectiveness increased over time in the post-treatment years for the crash categories with two important indicators of effectiveness, i.e., total and PDO, and rear-end crashes. Nevertheless, the treatment effectiveness on rear-end crashes is observed to decline with post-treatment time, although the base value is still less than one for all the three years. In summary, the results suggest the usefulness of PCSs for drivers.

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