First Advisor

Christopher Monsere

Date of Publication

Winter 2-19-2015

Document Type

Thesis

Degree Name

Master of Science (M.S.) in Civil & Environmental Engineering

Department

Civil and Environmental Engineering

Language

English

Subjects

Traffic surveys -- Mathematical models, Traffic estimation -- Mathematical models, Signalized intersections -- Safety measures, Roads -- Interchanges and intersections -- Safety measures, Traffic engineering

DOI

10.15760/etd.2193

Physical Description

1 online resource (xi, 115 pages)

Abstract

This effort seeks to answer the question; can a transferable model be developed from easily obtainable, publicly available land-use, census, roadway, and network data for the use in safety performance functions? 474 stop-controlled minor legs were used as the training data set using ordinary least squares regression. A best-fit model of maximum independent variables, n=12 was chosen using an exhaustive approach using Mallow's Cp to select the model with least bias in the predictors. The results of the analysis revealed that the combination of variables from Washington, Ohio, and North Carolina did not have a strong relationship. The best-fit model incorporated functional class information of the major-leg, minor leg functional class information, longitudinal markings, access to a parking lot, and population density of census tract. Validation of the model demonstrated an average 59 percent error between the model estimated and actual AADT values for validation data set (n=54). Furthermore, separate models for each state revealed a lack of uniformity in the dependent variables, and more variance description of the state specific AADT.

Rights

In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).

Comments

Please note that this PDF is large and slow to download.

Persistent Identifier

http://archives.pdx.edu/ds/psu/14615

Download

Share

COinS