Nowadays, technology is employed in many safety applications and countermeasures that would enhance traffic safety by influencing some crash-related factors. Therefore, crash-related factors must be determined for every roadway element by the development of safety performance functions. Safety performance functions (SPF) are employed to predict crash counts at the different roadway elements. Several SPFs have been developed for the various roadway elements based on different classifications such as functional classification and area type. Since a more detailed classification of roadway elements leads to more accurate crash predictions, multiple states have developed new system to categorize roads based on a comprehensive classification. In Florida, the new roadway context classification system incorporates geographic, demographic, and road characteristics information. In this study, SPFs have been developed in the framework of the FDOT roadway context classification system at three levels of modeling, context classification (CC-SPFs), area type (AT-SPFs), and statewide (SW-SPF) levels. Crash and traffic data of 2015-2019 years have been obtained. Road characteristics and road environment information have been also gathered along Florida roads for the SPF development. The developed SPFs showed that there are several variables that influence the frequency of crashes, such as annual average daily traffic (AADT), signalized intersections and access points densities, speed limit, and shoulder width. However, there are other variables that did not have an influence on crash occurrence such as concrete surface and the presence of bicycle slots. CC-SPFs had the best performance among others. Moreover, network screening to determine the most problematic road segments has been accomplished. The results of the network screening indicated that the most problematic roads in Florida are suburban commercial and urban general roads.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-1632 |
| Date | 01 January 2021 |
| Creators | Al-Omari, Ma'en |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations, 2020- |
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