Return to search

SEGMENTATION STRATEGIES FOR ROAD SAFETY ANALYSIS

This dissertation addresses the relationship between roadway segment length and roadway attributes and their relationship to the efficacy of Safety Performance Function (SPF) models. This research focuses on three aspects of segmentation: segment length, roadway attributes, and combinations of the two. First, it is shown that choice of average roadway segment length can result in markedly different priority lists. This leads to an investigation of the effect of segment length on the development of SPFs and identifies average lengths that produce the best-fitting SPF. Secondly, roadway attributes are filtered to test the effect that homogeneity has on SPF development. Lastly, a combination of segment length and attributes are examined in the same context.
In the process of conducting this research a tool was developed that provides objective goodness-of-fit measures as well as visual depictions of the model. This information can be used to avoid things like omitted variable bias by allowing the user to include other variables or filter the database. This dissertation also discusses and offers examples of ways to improve the models by employing alternate model forms.
This research revealed that SPF development is sensitive to a variety of factors related to segment length and attributes. It is clear that strict base condition filters based on the most predominant roadway attributes provide the best models. The preferred functional form was shown to be dependent on the segmentation approach (fixed versus variable length). Overall, an important step in SPF development process is evaluation and comparison to determine the ideal length and attributes for the network being analyzed (about 2 miles or 3.2 km for Kentucky parkways). As such, a framework is provided to help safety professionals employ the findings from this research.

Identiferoai:union.ndltd.org:uky.edu/oai:uknowledge.uky.edu:ce_etds-1066
Date01 January 2018
CreatorsGreen, Eric R.
PublisherUKnowledge
Source SetsUniversity of Kentucky
Detected LanguageEnglish
Typetext
Formatapplication/pdf
SourceTheses and Dissertations--Civil Engineering

Page generated in 0.0025 seconds