Arterial roads constitute the majority of the centerline miles of the Florida State Highway System. Severe injury involvements on these roads account for a quarter of the total severe injuries reported statewide. This research focuses on driver injury severity analysis of statewide multilane high speed arterials using crash data for the years 2002 to 2004. The first goal is to test different ways of analyzing crash data (by road entity and crash types) and find the best method of driver injury severity analysis. A second goal is to find driver, vehicle, road and environment related factors that contribute to severe involvements on multilane arterials. Exploratory analysis using one year of crash data (2004) using binary logit regression was used to measure the risk of driver severe injury given that a crash occurs. A preliminary list of significant factors was obtained. A massive data preparation effort was undertaken and a random sample of multivehicle crashes was selected for final analysis. The final injury severity analysis consisted of six road entity models and twenty crash type models. The data preparation and sampling was successful in allowing a robust dataset. The overall model was a good candidate for the analysis of driver injury severity on multilane high speed roads. Driver injury severity resulting from angle and left turn crashes were best modeled by separate non-signalized intersection crash analysis. Injury severity from rear end and fixed object crashes was best modeled by combined analysis of pure segment and non-signalized intersection crashes. The most important contributing factors found in the overall analysis included driver related variables such as age, gender, seat belt use, at-fault driver, physical defects and speeding. Crash and vehicle related contributing factors included driver ejection, collision type (harmful event), contributing cause, type of vehicle and off roadway crash. Multivehicle crashes and interactions with intersection and off road crashes were also significant. The most significant roadway related variables included speed limit, ADT per lane, access class, lane width, roadway curve, sidewalk width, non-high mast lighting density, type of friction course and skid resistance. The overall model had a very good fit but some misspecification symptoms appeared due to major differences in road entities and crash types by land use. Two additional models of crashes for urban and rural areas were successfully developed. The land use models' goodness of fit was substantially better than any other combination by road entity or the overall model. Their coefficients were substantially robust and their values agreed with scientific or empirical principles. Additional research is needed to prove these results for crash type models found most reliable by this investigation. A framework for injury severity analysis and safety improvement guidelines based on the results is presented. Additional integration of road characteristics (especially intersection) data is recommended for future research. Also, the use of statistical methods that account for correlation among crashes and locations are suggested for use in future research.

Since its inception in 1965, the Level-of-Service (LOS) has proved to be an important and practical "quality of service" indicator for transportation facilities around the world, widely used in the transportation and planning fields. The LOS rates these facilities' traffic operating conditions through the following delay-based indicators (ordered from best to worst conditions): A, B, C, D, E and F. This LOS rating has its foundation on quantifiable measures of effectiveness (MOEs) and on road users' perceptions; altogether, these measures define a LOS based on acceptable traffic operating conditions for the road user, implying that traffic safety is inherent to this definition. However, since 1994 safety has been excluded from the LOS definition since it cannot be quantified nor explicitly defined. The latter has been the motivation for research based on the LOS-Safety relationship, conducted at the University of Central Florida (UCF). Using data from two of the most studied transportation facility types within the field of traffic safety, signalized intersections and multilane high-speed arterial corridors, the research conducted has the following main objectives: to incorporate the LOS as a parameter in several traffic safety models, to extend the methodology adopted in previous studies to the subject matter, and to provide a platform for future transportation-related research on the LOS-Safety relationship. A meticulous data collection and preparation process was performed for the two LOS-Safety studies comprising this research. Apart from signalized intersections' and multilane-high speed arterial corridors' data, the other required types of information corresponded to crashes and road features, both obtained from FDOT's respective databases. In addition, the Highway Capacity Software (HCS) and the ArcGIS software package were extensively used for the data preparation. The result was a representative and robust dataset for each LOS-Safety study, to be later tested and analyzed with appropriate statistical methods. Regarding the LOS-Safety study for signalized intersections, two statistical techniques were used. The Generalized Estimating Equations (GEEs), the first technique, was used for the analyses considering all periods of a regular weekday (i.e. Monday through Friday): Early Morning, A.M. Peak, Midday, P.M. Peak and Late Evening; the second technique considered was the Negative Binomial, which was used for performing an individual analysis per period of the day. On the other hand, the LOS-Safety study for multilane high-speed arterial corridors made exclusive use of the Negative Binomial technique. An appropriate variable selection process was required for the respective model building and calibration procedures; the resulting models were built upon the six following response variables: total crashes, severe crashes, as well as rear-end, sideswipe, head-on and angle plus left-turn crashes. The final results proved to be meaningful for the understanding of traffic congestion effects on road safety, and on how they could be useful within the transportation planning scope. Overall, it was found that the risk for crash occurrence at signalized intersections and multilane high-speed arterial corridors is quite high between stable and unacceptable operating conditions; it was also found that this risk increases as it becomes later in the day. Among the significant factors within the signalized intersection-related models were LOS for the intersection as a whole, cycle length, lighting conditions, land use, traffic volume (major and minor roads), left-turn traffic volume (major road only), posted speed limit (major and minor roads), total number of through lanes (major and minor roads), overall total and total number of left-turn lanes (major road only), as well as county and period of the day (dummy variables). For multilane-high speed arterial corridors, the final models included LOS for the road section, average daily traffic (ADT), total number of through lanes in a single direction, total length of the road section, pavement surface type, as well as median and inside shoulder widths. A summary of the overall results per study, model implications and each LOS indicator is presented. Some of the final recommendations are to develop models for other crash types, to perform a LOS-Safety analysis at the approach-level for signalized intersections, as well as one that incorporates intersections within the arterial corridors' framework.