Advanced methodologies in dynamic traffic assignment modeling of managed lanes

Managed lane strategies are innovative road operation schemes for addressing congestion problems. These strategies operate a lane (lanes) adjacent to a freeway that provides congestion-free trips to eligible users, such as transit or toll-payers. To ensure the successful implementation of managed lanes, the demand on these lanes need to be accurately estimated. Among different approaches for predicting this demand, the four-step demand forecasting process is most common. Managed lane demand is usually estimated at the assignment step. Therefore, the key to reliably estimating the demand is the utilization of effective assignment modeling processes. ^ Managed lanes are particularly effective when the road is functioning at near-capacity. Therefore, capturing variations in demand and network attributes and performance is crucial for their modeling, monitoring and operation. As a result, traditional modeling approaches, such as those used in static traffic assignment of demand forecasting models, fail to correctly predict the managed lane demand and the associated system performance. The present study demonstrates the power of the more advanced modeling approach of dynamic traffic assignment (DTA), as well as the shortcomings of conventional approaches, when used to model managed lanes in congested environments. In addition, the study develops processes to support an effective utilization of DTA to model managed lane operations. ^ Static and dynamic traffic assignments consist of demand, network, and route choice model components that need to be calibrated. These components interact with each other, and an iterative method for calibrating them is needed. In this study, an effective standalone framework that combines static demand estimation and dynamic traffic assignment has been developed to replicate real-world traffic conditions. ^ With advances in traffic surveillance technologies collecting, archiving, and analyzing traffic data is becoming more accessible and affordable. The present study shows how data from multiple sources can be integrated, validated, and best used in different stages of modeling and calibration of managed lanes. Extensive and careful processing of demand, traffic, and toll data, as well as proper definition of performance measures, result in a calibrated and stable model, which closely replicates real-world congestion patterns, and can reasonably respond to perturbations in network and demand properties.^

Advanced Methodologies in Dynamic Traffic Assignment Modeling of Managed Lanes

Managed lane strategies are innovative road operation schemes for addressing congestion problems. These strategies operate a lane (lanes) adjacent to a freeway that provides congestion-free trips to eligible users, such as transit or toll-payers. To ensure the successful implementation of managed lanes, the demand on these lanes need to be accurately estimated. Among different approaches for predicting this demand, the four-step demand forecasting process is most common. Managed lane demand is usually estimated at the assignment step. Therefore, the key to reliably estimating the demand is the utilization of effective assignment modeling processes. Managed lanes are particularly effective when the road is functioning at near-capacity. Therefore, capturing variations in demand and network attributes and performance is crucial for their modeling, monitoring and operation. As a result, traditional modeling approaches, such as those used in static traffic assignment of demand forecasting models, fail to correctly predict the managed lane demand and the associated system performance. The present study demonstrates the power of the more advanced modeling approach of dynamic traffic assignment (DTA), as well as the shortcomings of conventional approaches, when used to model managed lanes in congested environments. In addition, the study develops processes to support an effective utilization of DTA to model managed lane operations. Static and dynamic traffic assignments consist of demand, network, and route choice model components that need to be calibrated. These components interact with each other, and an iterative method for calibrating them is needed. In this study, an effective standalone framework that combines static demand estimation and dynamic traffic assignment has been developed to replicate real-world traffic conditions. With advances in traffic surveillance technologies collecting, archiving, and analyzing traffic data is becoming more accessible and affordable. The present study shows how data from multiple sources can be integrated, validated, and best used in different stages of modeling and calibration of managed lanes. Extensive and careful processing of demand, traffic, and toll data, as well as proper definition of performance measures, result in a calibrated and stable model, which closely replicates real-world congestion patterns, and can reasonably respond to perturbations in network and demand properties.

Determinants of confidence and their effect on epistemic judgments: Implications for auditor calibration bias

Auditor decisions regarding the causes of accounting misstatements can have an audit effectiveness and efficiency. Specifically, overconfidence in one's decision can lead to an ineffective audit, whereas underconfidence in one's decision can lead to an inefficient audit. This dissertation explored the implications of providing various types of information cues to decision-makers regarding an Analytical Procedure task and investigated the relationship between different types of evidence cues (confirming, disconfirming, redundant or non-redundant) and the reduction in calibration bias. Information was collected using a laboratory experiment, from 45 accounting students participants. Research questions were analyzed using a 2 x 2 x 2 between-subject and within-subject analysis of covariance (ANCOVA). ^ Results indicated that presenting subjects with information cues dissimilar to the choice they made is an effective intervention in reducing the common overconfidence found in decision-making. In addition, other information characteristics, specifically non-redundant information can help in reducing a decision-maker's overconfidence/calibration bias for difficulty (compared to easy) decision-tasks. ^

The development of an optimized system of narcotic and explosive contraband mimics for calibration and training of biological detectors

Current commercially available mimics contain varying amounts of either the actual explosive/drug or the chemical compound of suspected interest by biological detectors. As a result, there is significant interest in determining the dominant chemical odor signatures of the mimics, often referred to as pseudos, particularly when compared to the genuine contraband material. This dissertation discusses results obtained from the analysis of drug and explosive headspace related to the odor profiles as recognized by trained detection canines. Analysis was performed through the use of headspace solid phase microextraction in conjunction with gas chromatography mass spectrometry (HS-SPME-GC-MS). Upon determination of specific odors, field trials were held using a combination of the target odors with COMPS. Piperonal was shown to be a dominant odor compound in the headspace of some ecstasy samples and a recognizable odor mimic by trained detection canines. It was also shown that detection canines could be imprinted on piperonal COMPS and correctly identify ecstasy samples at a threshold level of approximately 100ng/s. Isosafrole and/or MDP-2-POH show potential as training aid mimics for non-piperonal based MDMA. Acetic acid was shown to be dominant in the headspace of heroin samples and verified as a dominant odor in commercial vinegar samples; however, no common, secondary compound was detected in the headspace of either. Because of the similarities detected within respective explosive classes, several compounds were chosen for explosive mimics. A single based smokeless powder with a detectable level of 2,4-dinitrotoluene, a double based smokeless powder with a detectable level of nitroglycerine, 2-ethyl-1-hexanol, DMNB, ethyl centralite and diphenylamine were shown to be accurate mimics for TNT-based explosives, NG-based explosives, plastic explosives, tagged explosives, and smokeless powders, respectively. The combination of these six odors represents a comprehensive explosive odor kit with positive results for imprint on detection canines. As a proof of concept, the chemical compound PFTBA showed promise as a possible universal, non-target odor compound for comparison and calibration of detection canines and instrumentation. In a comparison study of shape versus vibration odor theory, the detection of d-methyl benzoate and methyl benzoate was explored using canine detectors. While results did not overwhelmingly substantiate either theory, shape odor theory provides a better explanation of the canine and human subject responses.

Headspace analysis of smokeless powders: Development of mass calibration methods using microdrop printing for chromatographic and ion mobility spectrometric detection

Smokeless powder additives are usually detected by their extraction from post-blast residues or unburned powder particles followed by analysis using chromatographic techniques. This work presents the first comprehensive study of the detection of the volatile and semi-volatile additives of smokeless powders using solid phase microextraction (SPME) as a sampling and pre-concentration technique. Seventy smokeless powders were studied using laboratory based chromatography techniques and a field deployable ion mobility spectrometer (IMS). The detection of diphenylamine, ethyl and methyl centralite, 2,4-dinitrotoluene, diethyl and dibutyl phthalate by IMS to associate the presence of these compounds to smokeless powders is also reported for the first time. A previously reported SPME-IMS analytical approach facilitates rapid sub-nanogram detection of the vapor phase components of smokeless powders. A mass calibration procedure for the analytical techniques used in this study was developed. Precise and accurate mass delivery of analytes in picoliter volumes was achieved using a drop-on-demand inkjet printing method. Absolute mass detection limits determined using this method for the various analytes of interest ranged between 0.03–0.8 ng for the GC-MS and between 0.03–2 ng for the IMS. Mass response graphs generated for different detection techniques help in the determination of mass extracted from the headspace of each smokeless powder. The analyte mass present in the vapor phase was sufficient for a SPME fiber to extract most analytes at amounts above the detection limits of both chromatographic techniques and the ion mobility spectrometer. Analysis of the large number of smokeless powders revealed that diphenylamine was present in the headspace of 96% of the powders. Ethyl centralite was detected in 47% of the powders and 8% of the powders had methyl centralite available for detection from the headspace sampling of the powders by SPME. Nitroglycerin was the dominant peak present in the headspace of the double-based powders. 2,4-dinitrotoluene which is another important headspace component was detected in 44% of the powders. The powders therefore have more than one headspace component and the detection of a combination of these compounds is achievable by SPME-IMS leading to an association to the presence of smokeless powders.

Sales tax enforcement: An empirical analysis of compliance enforcement methodologies and pathologies

Most research on tax evasion has focused on the income tax. Sales tax evasion has been largely ignored and dismissed as immaterial. This paper explored the differences between income tax and sales tax evasion and demonstrated that sales tax enforcement is deserving of and requires the use of different tools to achieve compliance. Specifically, the major enforcement problem with sales tax is not evasion: it is theft perpetrated by companies that act as collection agents for the state. Companies engage in a principal-agent relationship with the state and many retain funds collected as an agent of the state for private use. As such, the act of sales tax theft bears more resemblance to embezzlement than to income tax evasion. It has long been assumed that the sales tax is nearly evasion free, and state revenue departments report voluntary compliance in a manner that perpetuates this myth. Current sales tax compliance enforcement methodologies are similar in form to income tax compliance enforcement methodologies and are based largely on trust. The primary focus is on delinquent filers with a very small percentage of businesses subject to audit. As a result, there is a very large group of noncompliant businesses who file on time and fly below the radar while stealing millions of taxpayer dollars. ^ The author utilized a variety of statistical methods with actual field data derived from operations of the Southern Region Criminal Investigations Unit of the Florida Department of Revenue to evaluate current and proposed sales tax compliance enforcement methodologies in a quasi-experimental, time series research design and to set forth a typology of sales tax evaders. This study showed that current estimates of voluntary compliance in sales tax systems are seriously and significantly overstated and that current enforcement methodologies are inadequate to identify the majority of violators and enforce compliance. Sales tax evasion is modeled using the theory of planned behavior and Cressey’s fraud triangle and it is demonstrated that proactive enforcement activities, characterized by substantial contact with non-delinquent taxpayers, results in superior ability to identify noncompliance and provides a structure through which noncompliant businesses can be rehabilitated.^

The inclusion of differentially assessed lands in urban growth model calibration: a comparison of two approaches using SLEUTH

This research sought to understand the role that differentially assessed lands (lands in the United States given tax breaks in return for their guarantee to remain in agriculture) play in influencing urban growth. Our method was to calibrate the SLEUTH urban growth model under two different conditions. The first used an excluded layer that ignored such lands, effectively rendering them available for development. The second treated those lands as totally excluded from development. Our hypothesis was that excluding those lands would yield better metrics of fit with past data. Our results validate our hypothesis since two different metrics that evaluate goodness of fit both yielded higher values when differentially assessed lands are treated as excluded. This suggests that, at least in our study area, differential assessment, which protects farm and ranch lands for tenuous periods of time, has indeed allowed farmland to resist urban development. Including differentially assessed lands also yielded very different calibrated coefficients of growth as the model tried to account for the same growth patterns over two very different excluded areas. Excluded layer design can greatly affect model behavior. Since differentially assessed lands are quite common through the United States and are often ignored in urban growth modeling, the findings of this research can assist other urban growth modelers in designing excluded layers that result in more accurate model calibration and thus forecasting.

Slot Machines: Methodologies and Myths

The proliferation of legalized gaming has significantly changed the nature of the hospitality industry. While several aspects of gaming have flourished, none has become more popular, profitable, or technologically advanced as the slot machine. While more than half of all casino gambling, and earnings, is generated by slot machines, little has been written about the technology integral to these devices. The author describes the workings of computer-controlled slot machines and exposes some of the popular operating myths.

The development of calibrants through characterization of volatile organic compounds from peroxide based explosives and a non-target chemical calibration compound

Detection canines represent the fastest and most versatile means of illicit material detection. This research endeavor in its most simplistic form is the improvement of detection canines through training, training aids, and calibration. This study focuses on developing a universal calibration compound for which all detection canines, regardless of detection substance, can be tested daily to ensure that they are working with acceptable parameters. Surrogate continuation aids (SCAs) were developed for peroxide based explosives along with the validation of the SCAs already developed within the International Forensic Research Institute (IFRI) prototype surrogate explosives kit. Storage parameters of the SCAs were evaluated to give recommendations to the detection canine community on the best possible training aid storage solution that minimizes the likelihood of contamination. Two commonly used and accepted detection canine imprinting methods were also evaluated for the speed in which the canine is trained and their reliability. As a result of the completion of this study, SCAs have been developed for explosive detection canine use covering: peroxide based explosives, TNT based explosives, nitroglycerin based explosives, tagged explosives, plasticized explosives, and smokeless powders. Through the use of these surrogate continuation aids a more uniform and reliable system of training can be implemented in the field than is currently used today. By examining the storage parameters of the SCAs, an ideal storage system has been developed using three levels of containment for the reduction of possible contamination. The developed calibration compound will ease the growing concerns over the legality and reliability of detection canine use by detailing the daily working parameters of the canine, allowing for Daubert rules of evidence admissibility to be applied. Through canine field testing, it has been shown that the IFRI SCAs outperform other commercially available training aids on the market. Additionally, of the imprinting methods tested, no difference was found in the speed in which the canines are trained or their reliability to detect illicit materials. Therefore, if the recommendations discovered in this study are followed, the detection canine community will greatly benefit through the use of scientifically validated training techniques and training aids.

Novel analytical methodologies for the monitoring of traditional and non-traditional pollutants in different environmental compartments of South Florida

Routine monitoring of environmental pollution demands simplicity and speed without sacrificing sensitivity or accuracy. The development and application of sensitive, fast and easy to implement analytical methodologies for detecting emerging and traditional water and airborne contaminants in South Florida is presented. A novel method was developed for quantification of the herbicide glyphosate based on lyophilization followed by derivatization and simultaneous detection by fluorescence and mass spectrometry. Samples were analyzed from water canals that will hydrate estuarine wetlands of Biscayne National Park, detecting inputs of glyphosate from both aquatic usage and agricultural runoff from farms. A second study describes a set of fast, automated LC-MS/MS protocols for the analysis of dioctyl sulfosuccinate (DOSS) and 2-butoxyethanol, two components of Corexit®. Around 1.8 million gallons of those dispersant formulations were used in the response efforts for the Gulf of Mexico oil spill in 2010. The methods presented here allow the trace-level detection of these compounds in seawater, crude oil and commercial dispersants formulations. In addition, two methodologies were developed for the analysis of well-known pollutants, namely Polycyclic Aromatic Hydrocarbons (PAHs) and airborne particulate matter (APM). PAHs are ubiquitous environmental contaminants and some are potent carcinogens. Traditional GC-MS analysis is labor-intensive and consumes large amounts of toxic solvents. My study provides an alternative automated SPE-LC-APPI-MS/MS analysis with minimal sample preparation and a lower solvent consumption. The system can inject, extract, clean, separate and detect 28 PAHs and 15 families of alkylated PAHs in 28 minutes. The methodology was tested with environmental samples from Miami. Airborne Particulate Matter is a mixture of particles of chemical and biological origin. Assessment of its elemental composition is critical for the protection of sensitive ecosystems and public health. The APM collected from Port Everglades between 2005 and 2010 was analyzed by ICP-MS after acid digestion of filters. The most abundant elements were Fe and Al, followed by Cu, V and Zn. Enrichment factors show that hazardous elements (Cd, Pb, As, Co, Ni and Cr) are introduced by anthropogenic activities. Data suggest that the major sources of APM were an electricity plant, road dust, industrial emissions and marine vessels.

Experimental and Analytical Methodologies for Predicting Peak Loads on Building Envelopes and Roofing Systems

The performance of building envelopes and roofing systems significantly depends on accurate knowledge of wind loads and the response of envelope components under realistic wind conditions. Wind tunnel testing is a well-established practice to determine wind loads on structures. For small structures much larger model scales are needed than for large structures, to maintain modeling accuracy and minimize Reynolds number effects. In these circumstances the ability to obtain a large enough turbulence integral scale is usually compromised by the limited dimensions of the wind tunnel meaning that it is not possible to simulate the low frequency end of the turbulence spectrum. Such flows are called flows with Partial Turbulence Simulation. In this dissertation, the test procedure and scaling requirements for tests in partial turbulence simulation are discussed. A theoretical method is proposed for including the effects of low-frequency turbulences in the post-test analysis. In this theory the turbulence spectrum is divided into two distinct statistical processes, one at high frequencies which can be simulated in the wind tunnel, and one at low frequencies which can be treated in a quasi-steady manner. The joint probability of load resulting from the two processes is derived from which full-scale equivalent peak pressure coefficients can be obtained. The efficacy of the method is proved by comparing predicted data derived from tests on large-scale models of the Silsoe Cube and Texas-Tech University buildings in Wall of Wind facility at Florida International University with the available full-scale data. For multi-layer building envelopes such as rain-screen walls, roof pavers, and vented energy efficient walls not only peak wind loads but also their spatial gradients are important. Wind permeable roof claddings like roof pavers are not well dealt with in many existing building codes and standards. Large-scale experiments were carried out to investigate the wind loading on concrete pavers including wind blow-off tests and pressure measurements. Simplified guidelines were developed for design of loose-laid roof pavers against wind uplift. The guidelines are formatted so that use can be made of the existing information in codes and standards such as ASCE 7-10 on pressure coefficients on components and cladding.

The Development of an Optimized System of Narcotic and Explosive Contraband Mimics for Calibration and Training of Biological Detectors

Current commercially available mimics contain varying amounts of either the actual explosive/drug or the chemical compound of suspected interest by biological detectors. As a result, there is significant interest in determining the dominant chemical odor signatures of the mimics, often referred to as pseudos, particularly when compared to the genuine contraband material. This dissertation discusses results obtained from the analysis of drug and explosive headspace related to the odor profiles as recognized by trained detection canines. Analysis was performed through the use of headspace solid phase microextraction in conjunction with gas chromatography mass spectrometry (HS-SPME-GC-MS). Upon determination of specific odors, field trials were held using a combination of the target odors with COMPS. Piperonal was shown to be a dominant odor compound in the headspace of some ecstasy samples and a recognizable odor mimic by trained detection canines. It was also shown that detection canines could be imprinted on piperonal COMPS and correctly identify ecstasy samples at a threshold level of approximately 100ng/s. Isosafrole and/or MDP-2-POH show potential as training aid mimics for non-piperonal based MDMA. Acetic acid was shown to be dominant in the headspace of heroin samples and verified as a dominant odor in commercial vinegar samples; however, no common, secondary compound was detected in the headspace of either. Because of the similarities detected within respective explosive classes, several compounds were chosen for explosive mimics. A single based smokeless powder with a detectable level of 2,4-dinitrotoluene, a double based smokeless powder with a detectable level of nitroglycerine, 2-ethyl-1-hexanol, DMNB, ethyl centralite and diphenylamine were shown to be accurate mimics for TNT-based explosives, NG-based explosives, plastic explosives, tagged explosives, and smokeless powders, respectively. The combination of these six odors represents a comprehensive explosive odor kit with positive results for imprint on detection canines. As a proof of concept, the chemical compound PFTBA showed promise as a possible universal, non-target odor compound for comparison and calibration of detection canines and instrumentation. In a comparison study of shape versus vibration odor theory, the detection of d-methyl benzoate and methyl benzoate was explored using canine detectors. While results did not overwhelmingly substantiate either theory, shape odor theory provides a better explanation of the canine and human subject responses.

Headspace Analysis of Smokeless Powders: Development of Mass Calibration Methods using Microdrop Printing for Chromatographic and Ion Mobility Spectrometric Detection

Smokeless powder additives are usually detected by their extraction from post-blast residues or unburned powder particles followed by analysis using chromatographic techniques. This work presents the first comprehensive study of the detection of the volatile and semi-volatile additives of smokeless powders using solid phase microextraction (SPME) as a sampling and pre-concentration technique. Seventy smokeless powders were studied using laboratory based chromatography techniques and a field deployable ion mobility spectrometer (IMS). The detection of diphenylamine, ethyl and methyl centralite, 2,4-dinitrotoluene, diethyl and dibutyl phthalate by IMS to associate the presence of these compounds to smokeless powders is also reported for the first time. A previously reported SPME-IMS analytical approach facilitates rapid sub-nanogram detection of the vapor phase components of smokeless powders. A mass calibration procedure for the analytical techniques used in this study was developed. Precise and accurate mass delivery of analytes in picoliter volumes was achieved using a drop-on-demand inkjet printing method. Absolute mass detection limits determined using this method for the various analytes of interest ranged between 0.03 - 0.8 ng for the GC-MS and between 0.03 - 2 ng for the IMS. Mass response graphs generated for different detection techniques help in the determination of mass extracted from the headspace of each smokeless powder. The analyte mass present in the vapor phase was sufficient for a SPME fiber to extract most analytes at amounts above the detection limits of both chromatographic techniques and the ion mobility spectrometer. Analysis of the large number of smokeless powders revealed that diphenylamine was present in the headspace of 96% of the powders. Ethyl centralite was detected in 47% of the powders and 8% of the powders had methyl centralite available for detection from the headspace sampling of the powders by SPME. Nitroglycerin was the dominant peak present in the headspace of the double-based powders. 2,4-dinitrotoluene which is another important headspace component was detected in 44% of the powders. The powders therefore have more than one headspace component and the detection of a combination of these compounds is achievable by SPME-IMS leading to an association to the presence of smokeless powders.

Improved Criteria for Estimating Calibration Factors for Highway Safety Manual (HSM) Applications

The Highway Safety Manual (HSM) estimates roadway safety performance based on predictive models that were calibrated using national data. Calibration factors are then used to adjust these predictive models to local conditions for local applications. The HSM recommends that local calibration factors be estimated using 30 to 50 randomly selected sites that experienced at least a total of 100 crashes per year. It also recommends that the factors be updated every two to three years, preferably on an annual basis. However, these recommendations are primarily based on expert opinions rather than data-driven research findings. Furthermore, most agencies do not have data for many of the input variables recommended in the HSM. This dissertation is aimed at determining the best way to meet three major data needs affecting the estimation of calibration factors: (1) the required minimum sample sizes for different roadway facilities, (2) the required frequency for calibration factor updates, and (3) the influential variables affecting calibration factors. In this dissertation, statewide segment and intersection data were first collected for most of the HSM recommended calibration variables using a Google Maps application. In addition, eight years (2005-2012) of traffic and crash data were retrieved from existing databases from the Florida Department of Transportation. With these data, the effect of sample size criterion on calibration factor estimates was first studied using a sensitivity analysis. The results showed that the minimum sample sizes not only vary across different roadway facilities, but they are also significantly higher than those recommended in the HSM. In addition, results from paired sample t-tests showed that calibration factors in Florida need to be updated annually. To identify influential variables affecting the calibration factors for roadway segments, the variables were prioritized by combining the results from three different methods: negative binomial regression, random forests, and boosted regression trees. Only a few variables were found to explain most of the variation in the crash data. Traffic volume was consistently found to be the most influential. In addition, roadside object density, major and minor commercial driveway densities, and minor residential driveway density were also identified as influential variables.

The Development of Calibrants through Characterization of Volatile Organic Compounds from Peroxide Based Explosives and a Non-target Chemical Calibration Compound

Detection canines represent the fastest and most versatile means of illicit material detection. This research endeavor in its most simplistic form is the improvement of detection canines through training, training aids, and calibration. This study focuses on developing a universal calibration compound for which all detection canines, regardless of detection substance, can be tested daily to ensure that they are working with acceptable parameters. Surrogate continuation aids (SCAs) were developed for peroxide based explosives along with the validation of the SCAs already developed within the International Forensic Research Institute (IFRI) prototype surrogate explosives kit. Storage parameters of the SCAs were evaluated to give recommendations to the detection canine community on the best possible training aid storage solution that minimizes the likelihood of contamination. Two commonly used and accepted detection canine imprinting methods were also evaluated for the speed in which the canine is trained and their reliability. As a result of the completion of this study, SCAs have been developed for explosive detection canine use covering: peroxide based explosives, TNT based explosives, nitroglycerin based explosives, tagged explosives, plasticized explosives, and smokeless powders. Through the use of these surrogate continuation aids a more uniform and reliable system of training can be implemented in the field than is currently used today. By examining the storage parameters of the SCAs, an ideal storage system has been developed using three levels of containment for the reduction of possible contamination. The developed calibration compound will ease the growing concerns over the legality and reliability of detection canine use by detailing the daily working parameters of the canine, allowing for Daubert rules of evidence admissibility to be applied. Through canine field testing, it has been shown that the IFRI SCAs outperform other commercially available training aids on the market. Additionally, of the imprinting methods tested, no difference was found in the speed in which the canines are trained or their reliability to detect illicit materials. Therefore, if the recommendations discovered in this study are followed, the detection canine community will greatly benefit through the use of scientifically validated training techniques and training aids.