The identification of roadway sections with a higher than expected number of crashes is usually based on long term crash frequency data. In situations where historical crash data are limited or not available, surrogate safety measures, based on characteristics such as road geometries, traffic volume, and speed variation are often considered. Most of existing crash prediction models relate safety to speed variation at a specific point on the roadway. However, such point-specific explanatory variables do not capture the effect of speed consistency along the roadway. This study developed several measures based on the speed profiles along road segments to estimate the crash frequency on urban streets. To collect speed profile data, second-by-second speed data were obtained from more than 460 GPS-equipped vehicles participating in the Commute Atlanta Study over the 2004 calendar year. A series of speed data filters have been developed to identify likely free-flow speed data. The quantified relationships between surrogate measures and crash frequency are developed using regression tree and generalized linear modeling (GLM) approaches. The results indicate that safety characteristics of roadways are likely a function of the roadway classification. Two crash prediction models with different set of explanatory variables were developed for higher and lower classification roadways. The findings support the potential use of the profile-based measures to evaluate the safety of road network as the deployment of GPS-equipped vehicles become more prevalent.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/28275 |
| Date | 06 April 2009 |
| Creators | Boonsiripant, Saroch |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Detected Language | English |
| Type | Dissertation |
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