Thesis (Ph.D.)--Georgetown University, 2009. / Includes bibliographical references (p. 275-288)

Thesis (M.P.A.)--University of Hong Kong, 1993.

Thesis (M.P.A.)--University of Hong Kong, 1993. / Also available in print.

Thesis (Ph. D.)--Catholic University of America, 1932. / At head of title: The Catholic University of America. Vita. Bibliography: p. 185-189.

Thesis (M.C.E.)--University of Delaware, 2007. / Principal faculty advisor: Sue McNeil, Dept. of Civil & Environmental Engineering. Includes bibliographical references.

The work traces the early history of the Texas State Department of Highways. Beginning with the first efforts to create a department, the study focuses on the period between 1917 and 1923. Much attention goes to the legislative background of the early actions of the department. Subsequently, the work examines various statistical measures of the department's performance. This includes comparisons between Texas and nearby states, and the national highs, lows, and averages. Concluding the study is an examination of the department's immediate goals and long range plans in the years after 1923. The general conclusion of the study is that the department played a useful role in the development of state roads in Texas.

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.