Development of prediction models for freeway incident durations using data mining techniques

The nation's freeway systems are becoming increasingly congested. A major contribution to traffic congestion on freeways is due to traffic incidents. Traffic incidents are non-recurring events such as accidents or stranded vehicles that cause a temporary roadway capacity reduction, and they can account for as much as 60 percent of all traffic congestion on freeways. One major freeway incident management strategy involves diverting traffic to avoid incident locations by relaying timely information through Intelligent Transportation Systems (ITS) devices such as dynamic message signs or real-time traveler information systems. The decision to divert traffic depends foremost on the expected duration of an incident, which is difficult to predict. In addition, the duration of an incident is affected by many contributing factors. Determining and understanding these factors can help the process of identifying and developing better strategies to reduce incident durations and alleviate traffic congestion. A number of research studies have attempted to develop models to predict incident durations, yet with limited success. ^ This dissertation research attempts to improve on this previous effort by applying data mining techniques to a comprehensive incident database maintained by the District 4 ITS Office of the Florida Department of Transportation (FDOT). Two categories of incident duration prediction models were developed: "offline" models designed for use in the performance evaluation of incident management programs, and "online" models for real-time prediction of incident duration to aid in the decision making of traffic diversion in the event of an ongoing incident. Multiple data mining analysis techniques were applied and evaluated in the research. The multiple linear regression analysis and decision tree based method were applied to develop the offline models, and the rule-based method and a tree algorithm called M5P were used to develop the online models. ^ The results show that the models in general can achieve high prediction accuracy within acceptable time intervals of the actual durations. The research also identifies some new contributing factors that have not been examined in past studies. As part of the research effort, software code was developed to implement the models in the existing software system of District 4 FDOT for actual applications. ^

Hybrid multi-objective optimization and hybridized self-organizing response surface method

Numerical optimization is a technique where a computer is used to explore design parameter combinations to find extremes in performance factors. In multi-objective optimization several performance factors can be optimized simultaneously. The solution to multi-objective optimization problems is not a single design, but a family of optimized designs referred to as the Pareto frontier. The Pareto frontier is a trade-off curve in the objective function space composed of solutions where performance in one objective function is traded for performance in others. A Multi-Objective Hybridized Optimizer (MOHO) was created for the purpose of solving multi-objective optimization problems by utilizing a set of constituent optimization algorithms. MOHO tracks the progress of the Pareto frontier approximation development and automatically switches amongst those constituent evolutionary optimization algorithms to speed the formation of an accurate Pareto frontier approximation. Aerodynamic shape optimization is one of the oldest applications of numerical optimization. MOHO was used to perform shape optimization on a 0.5-inch ballistic penetrator traveling at Mach number 2.5. Two objectives were simultaneously optimized: minimize aerodynamic drag and maximize penetrator volume. This problem was solved twice. The first time the problem was solved by using Modified Newton Impact Theory (MNIT) to determine the pressure drag on the penetrator. In the second solution, a Parabolized Navier-Stokes (PNS) solver that includes viscosity was used to evaluate the drag on the penetrator. The studies show the difference in the optimized penetrator shapes when viscosity is absent and present in the optimization. In modern optimization problems, objective function evaluations may require many hours on a computer cluster to perform these types of analysis. One solution is to create a response surface that models the behavior of the objective function. Once enough data about the behavior of the objective function has been collected, a response surface can be used to represent the actual objective function in the optimization process. The Hybrid Self-Organizing Response Surface Method (HYBSORSM) algorithm was developed and used to make response surfaces of objective functions. HYBSORSM was evaluated using a suite of 295 non-linear functions. These functions involve from 2 to 100 variables demonstrating robustness and accuracy of HYBSORSM.

Monitoring of Tree Island Conditions in the Southern Everglades: The Effects of Hurricanes and Hydrology on the Status and Population Dynamics of Sixteen Tropical Hardwood Hammock Tree Islands

In 2005 we began a multi-year intensive monitoring and assessment study of tropical hardwood hammocks within two distinct hydrologic regions in Everglades National Park, under funding from the CERP Monitoring and Assessment Program. In serving as an Annual Report for 2010, this document, reports in detail on the population dynamics and status of tropical hardwood hammocks in Shark Slough and adjacent marl prairies during a 4-year period between 2005 and 2009. 2005-09 was a period that saw a marked drawdown in marsh water levels (July 2006 - July 2008), and an active hurricane season in 2005 with two hurricanes, Hurricane Katrina and Wilma, making landfall over south Florida. Thus much of our focus here is on the responses of these forests to annual variation in marsh water level, and on recovery from disturbance. Most of the data are from 16 rectangular permanent plots of 225-625 m2 , with all trees mapped and tagged, and bi-annual sampling of the tree, sapling, shrub, and herb layer in a nested design. At each visit, canopy photos were taken and later analyzed for determination of interannual variation in leaf area index and canopy openness. Three of the plots were sampled at 2-month intervals, in order to gain a better idea of seasonal dynamics in litterfall and litter turnover. Changes in canopy structure were monitored through a vertical line intercept method.

Development of a surface-enhanced raman spectroscopy method for the detection of benzodiazepines in urine

Benzodiazepines are among the most prescribed compounds for anti-anxiety and are present in many toxicological screens. These drugs are also prominent in the commission of drug facilitated sexual assaults due their effects on the central nervous system. Due to their potency, a low dose of these compounds is often administered to victims; therefore, the target detection limit for these compounds in biological samples is 10 ng/mL. Currently these compounds are predominantly analyzed using immunoassay techniques; however more specific screening methods are needed. ^ The goal of this dissertation was to develop a rapid, specific screening technique for benzodiazepines in urine samples utilizing surface-enhanced Raman spectroscopy (SERS), which has previously been shown be capable of to detect trace quantities of pharmaceutical compounds in aqueous solutions. Surface enhanced Raman spectroscopy has the advantage of overcoming the low sensitivity and fluorescence effects seen with conventional Raman spectroscopy. The spectra are obtained by applying an analyte onto a SERS-active metal substrate such as colloidal metal particles. SERS signals can be further increased with the addition of aggregate solutions. These agents cause the nanoparticles to amass and form hot-spots which increase the signal intensity. ^ In this work, the colloidal particles are spherical gold nanoparticles in aqueous solution with an average size of approximately 30 nm. The optimum aggregating agent for the detection of benzodiazepines was determined to be 16.7 mM MgCl2, providing the highest signal intensities at the lowest drug concentrations with limits of detection between 0.5 and 127 ng/mL. A supported liquid extraction technique was utilized as a rapid clean extraction for benzodiazepines from urine at a pH of 5.0, allowing for clean extraction with limits of detection between 6 and 640 ng/mL. It was shown that at this pH other drugs that are prevalent in urine samples can be removed providing the selective detection of the benzodiazepine of interest. ^ This technique has been shown to provide rapid (less than twenty minutes), sensitive, and specific detection of benzodiazepines at low concentrations in urine. It provides the forensic community with a sensitive and specific screening technique for the detection of benzodiazepines in drug facilitated assault cases.^

Validation of an Analytical Method for the Identification of SmartWater CSI Forensic Marking Technology

SmartWater is a chemical taggant used as a crime deterrent. The chemical taggant is a colorless liquid that fluoresces yellow under ultra-violet (UV) light and contains distinctive, identifiable and traceable elemental composition. For instance, upon a break and entry scenario, the burglar is sprayed with a solution that has an elemental signature custom-made to a specific location. The residues of this taggant persist on skin and other objects and can be easily recovered for further analysis. The product has been effectively used in Europe as a crime deterrent and has been recently introduced in South Florida. In 2014, Fourt Lauderdale Police Department reported the use of SmartWater products with a reduction in burglaries of 14% [1]. The International Forensic Research Institute (IFRI) at FIU validated the scientific foundation of the methods of recovery and analysis of these chemical tagging systems using LA-ICP-MS. Analytical figures of merit of the method such as precision, accuracy, limits of detection, linearity and selectivity are reported in this study. Moreover, blind samples were analyzed by LA-ICP-MS to compare the chemical signatures to the company’s database and evaluate error rates and the accuracy of the method. This study demonstrated that LA-ICP-MS could be used to effectively detect these traceable taggants to assist law enforcement agencies in the United States with cases involving transfer of these forensic coding systems.

Development of a Surface-Enhanced Raman Spectroscopy Method for the Detection of Benzodiazepines in Urine

Benzodiazepines are among the most prescribed compounds for anti-anxiety and are present in many toxicological screens. These drugs are also prominent in the commission of drug facilitated sexual assaults due their effects on the central nervous system. Due to their potency, a low dose of these compounds is often administered to victims; therefore, the target detection limit for these compounds in biological samples is 10 ng/mL. Currently these compounds are predominantly analyzed using immunoassay techniques; however more specific screening methods are needed. The goal of this dissertation was to develop a rapid, specific screening technique for benzodiazepines in urine samples utilizing surface-enhanced Raman spectroscopy (SERS), which has previously been shown be capable of to detect trace quantities of pharmaceutical compounds in aqueous solutions. Surface enhanced Raman spectroscopy has the advantage of overcoming the low sensitivity and fluorescence effects seen with conventional Raman spectroscopy. The spectra are obtained by applying an analyte onto a SERS-active metal substrate such as colloidal metal particles. SERS signals can be further increased with the addition of aggregate solutions. These agents cause the nanoparticles to amass and form hot-spots which increase the signal intensity. In this work, the colloidal particles are spherical gold nanoparticles in aqueous solution with an average size of approximately 30 nm. The optimum aggregating agent for the detection of benzodiazepines was determined to be 16.7 mM MgCl2, providing the highest signal intensities at the lowest drug concentrations with limits of detection between 0.5 and 127 ng/mL. A supported liquid extraction technique was utilized as a rapid clean extraction for benzodiazepines from urine at a pH of 5.0, allowing for clean extraction with limits of detection between 6 and 640 ng/mL. It was shown that at this pH other drugs that are prevalent in urine samples can be removed providing the selective detection of the benzodiazepine of interest. This technique has been shown to provide rapid (less than twenty minutes), sensitive, and specific detection of benzodiazepines at low concentrations in urine. It provides the forensic community with a sensitive and specific screening technique for the detection of benzodiazepines in drug facilitated assault cases.

Modeling of a Heat-Induced Buckling of Plates Using the Mesh-free Method

In the process of engineering design of structural shapes, the flat plate analysis results can be generalized to predict behaviors of complete structural shapes. In this case, the purpose of this project is to analyze a thin flat plate under conductive heat transfer and to simulate the temperature distribution, thermal stresses, total displacements, and buckling deformations. The current approach in these cases has been using the Finite Element Method (FEM), whose basis is the construction of a conforming mesh. In contrast, this project uses the mesh-free Scan Solve Method. This method eliminates the meshing limitation using a non-conforming mesh. I implemented this modeling process developing numerical algorithms and software tools to model thermally induced buckling. In addition, convergence analysis was achieved, and the results were compared with FEM. In conclusion, the results demonstrate that the method gives similar solutions to FEM in quality, but it is computationally less time consuming.

Meshfree Method for Prediction of Thermal Properties of Porous Ceramic Materials

In the presented thesis work, meshfree method with distance fields is applied to create a novel computational approach which enables inclusion of the realistic geometric models of the microstructure and liberates Finite Element Analysis(FEA) from thedependance on and limitations of meshing of fine microstructural feature such as splats and porosity.Manufacturing processes of ceramics produce materials with complex porosity microstructure.Geometry of pores, their size and location substantially affect macro scale physical properties of the material. Complex structure and geometry of the pores severely limit application of modern Finite Element Analysis methods because they require construction of spatial grids (meshes) that conform to the geometric shape of the structure. As a result, there are virtually no effective tools available for predicting overall mechanical and thermal properties of porous materials based on their microstructure. This thesis is a separate handling and controls of geometric and physical computational models that are seamlessly combined at solution run time. Using the proposedapproach we will determine the effective thermal conductivity tensor of real porous ceramic materials featuring both isotropic and anisotropic thermal properties. This work involved development and implementation of numerical algorithms, data structure, and software.

Development of a Coupling Model for Fluid-Structure Interaction using the Mesh-free Finite Element Method and the Lattice Boltzmann Method

In the presented thesis work, the meshfree method with distance fields was coupled with the lattice Boltzmann method to obtain solutions of fluid-structure interaction problems. The thesis work involved development and implementation of numerical algorithms, data structure, and software. Numerical and computational properties of the coupling algorithm combining the meshfree method with distance fields and the lattice Boltzmann method were investigated. Convergence and accuracy of the methodology was validated by analytical solutions. The research was focused on fluid-structure interaction solutions in complex, mesh-resistant domains as both the lattice Boltzmann method and the meshfree method with distance fields are particularly adept in these situations. Furthermore, the fluid solution provided by the lattice Boltzmann method is massively scalable, allowing extensive use of cutting edge parallel computing resources to accelerate this phase of the solution process. The meshfree method with distance fields allows for exact satisfaction of boundary conditions making it possible to exactly capture the effects of the fluid field on the solid structure.