Keywords

GIS, severe crashes, multilane corridors, state roads, county level, roadway level, spatial analysis, full Bayesian, Moran's I

Abstract

The research conducted in this thesis consists of a Geographic Information Systems (GIS) based safety study and a spatial analysis of vehicle crashes in the State of Florida. The GIS safety study is comprised of a County and Roadway Level GIS analysis of multilane corridors. The spatial analysis investigated the use of county-level vehicle crash models, taking spatial effects into account. The GIS safety study examines the locations of high trends of severe crashes (includes incapacitating and fatal crashes) on multilane corridors in the State of Florida at two levels, county level and roadway level. The GIS tool, which is used frequently in traffic safety research, was utilized to visually display those locations. At the county level, several maps of crash trends were generated. It was found that counties with high population and large metropolitan areas tend to have more crash occurrences. It was also found that the most severe crashes occurred in counties with more urban than rural roads. The neighboring counties of Pasco, Pinellas and Hillsborough had high severe crash rate per mile. At the roadway level, seven counties were chosen for the analysis based on their high severe crash trends, metropolitan size and geographical location. Several GIS maps displaying the safety level of multilane corridors in the seven counties were generated. The GIS maps were based on a ranking methodology that was developed in research that evaluated the safety condition of road segments and signalized intersections separately. The GIS maps were supported by Excel tables which provided details on the most hazardous locations on the roadways. The results of the roadway level analysis found that the worst corridors were located in Pasco, Pinellas and Hillsborough Counties. Also, a sliding window approach was developed and performed on the ten most hazardous corridors of the seven counties. The results were graphs locating the most dangerous 0.5 miles on a corridor. For the spatial analysis of crashes, the exploratory Moran's I statistic test revealed that crash related spatial clustering existed at the county level. For crash modeling, a full Bayesian (FB) hierarchical model is proposed to account for the possible spatial correlation among crash occurrence of adjacent counties. The spatial correlation is realized by specifying a Conditional Auto-regressive prior to the residual term of the link function in standard Poisson regression. Two FB models were developed, one for total crashes and one for severe crashes. The variables used include traffic related factors and socio-economic factors. Counties with higher road congestion levels, higher densities of arterials and intersections, higher percentage of population in the 15-24 age group and higher income levels have increased crash risk. Road congestion and higher education levels, however, were negatively correlated with the risk of severe crashes. The analysis revealed that crash related spatial correlation existed among the counties. The FB models were found to fit the data better than traditional methods such as Negative Binomial and that is primarily due to the existence of spatial correlation. Overall, this study provides the Transportation Agencies with specific information on where improvements must be implemented to have better safety conditions on the roads of Florida. The study also proves that neighboring counties are more likely to have similar crash trends than the more distant ones.

Notes

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Graduation Date

2009

Advisor

Abdel-Aty, Mohamed

Degree

Master of Science (M.S.)

College

College of Engineering and Computer Science

Department

Civil and Environmental Engineering

Degree Program

Civil Engineering

Format

application/pdf

Identifier

CFE0002623

URL

http://purl.fcla.edu/fcla/etd/CFE0002623

Language

English

Release Date

April 2009

Length of Campus-only Access

None

Access Status

Masters Thesis (Open Access)

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