<p> Average annual daily traffic (AADT) is important in transportation engineering and planning, and although the State of Louisiana collects AADT on a regular basis on state-maintained highways, most parishes and smaller municipalities do not have the resources to collect AADT frequently. Because the roads under the jurisdiction of parishes and municipalities account for three-fourths of the entire state road network, a practical method to estimated AADT must be developed. Before model development, previous studies into AADT estimation and their results are to be further analyzed. Roadway, demographic, and economic data for selected parishes in Louisiana is collected and processed to remove any data not necessary in model development, and afterwards, parish-specific and combination data models using this data are developed to compare to the observed AADT at a particular count station. Parish selection is based on population, number of existing count stations within the parish, and if an Interstate Highway traverses the parish. Because of the varying characteristics among the data in the selected parishes, parish-specific models for the rural parish roads are developed, and Poisson is selected as the regression model due to discrete data. Results for all Poisson models developed show that the models tend to overestimate AADT for lower observed AADT and underestimate AADT for higher observed AADT. Because of this, support vector regression (SVR) was used, and this method greatly improved the estimation of AADT in comparison to the Poisson regression as shown using certain goodness-of-fit parameters.</p>

<p> This thesis is part of current research at Center for Intelligence Systems Research (CISR) at The George Washington University for developing new in-vehicle warning systems via Brain-Computer Interfaces (BCIs). The purpose of conducting this research is to contribute to the current gap between BCI and in-vehicle safety studies. It is based on the premise that accurate and timely monitoring of human (driver) brain's signal to external stimuli could significantly aide in detection of driver's intentions and development of effective warning systems. The thesis starts with introducing the concept of BCI and its development history while it provides a literature review on the nature of brain signals. The current advancement and increasing demand for commercial and non-medical BCI products are described. In addition, the recent research attempts in transportation safety to study drivers' behavior or responses through brain signals are reviewed. The safety studies, which are focused on employing a reliable and practical BCI system as an in-vehicle assistive device, are also introduced. A major focus of this thesis research has been on the evaluation and development of the signal processing algorithms which can effectively filter and process brain signals when the human subject is subjected to Visual LED (Light Emitting Diodes) stimuli at different frequencies. The stimulated brain generates a voltage potential, referred to as Steady-State Visual Evoked Potential (SSVEP). Therefore, a newly modified analysis algorithm for detecting the brain visual signals is proposed. These algorithms are designed to reach a satisfactory accuracy rate without preliminary trainings, hence focusing on eliminating the need for lengthy training of human subjects. Another important concern is the ability of the algorithms to find correlation of brain signals with external visual stimuli in real-time. The developed analysis models are based on algorithms which are capable of generating results for real-time processing of BCI devices. All of these methods are evaluated through two sets of recorded brain signals which were recorded by g.TEC CO. as an external source and recorded brain signals during our car driving simulator experiments. The final discussion is about how the presence of an SSVEP based warning system could affect drivers' performances which is defined by their reaction distance and Time to Collision (TTC). Three different scenarios with and without warning LEDs were planned to measure the subjects' normal driving behavior and their performance while they use a warning system during their driving task. Finally, warning scenarios are divided into short and long warning periods without and with informing the subjects, respectively. The long warning period scenario attempts to determine the level of drivers' distraction or vigilance during driving. The good outcome of warning scenarios can bridge between vehicle safety studies and online BCI system design research. The preliminary results show some promise of the developed methods for in-vehicle safety systems. However, for any decisive conclusion that considers using a BCI system as a helpful in-vehicle assistive device requires far deeper scrutinizing.</p>

<p> Walking is underrepresented in large area models of urban behavior, largely due to difficulty in obtaining data and computational issues in representing land use at such a small scale. Recent advances in data availability, like the ubiquitous point-of-interest data collected by many private companies, as well as a worldwide dataset of local streets in OpenStreetMap, a standard format for obtaining transit schedules in GTFS, etc, provide the potential to build a scalable methodology to understand travel behavior at a pedestrian scale which can be applied wherever these datasets are available. </p><p> This dissertation improves on similar indexes like WalkScore by estimating a model that represents the substitution of destinations around a location and between the modes of walking, automobile, and transit. This model is estimated using the San Francisco Bay Area portion of the 2012 California Household Travel Survey to capture observed transportation behavior, and accounts for the demographics included in the survey. These representations of travel behavior can then be used as right-hand side variables in other urban models: for instance, to create a residential location choice model where measures of accessibility and available demographics are used to understand why people choose to live where they do. </p><p> This dissertation is organized into four topics, one for each of chapters 2-5. The first topic establishes a framework for measuring the network of destination opportunities in the city for each of the walking, transit, and auto transportation modes. Destinations in the form of parcels and buildings, businesses, population, and points of interest are tied to each network so that the distance from each location to every destination can be computed by mode. The use of a points-of-interest dataset as the set of public-facing destinations is novel in the context of a traditional travel demand destination model. </p><p> This chapter also creates a case study model of trip generation for home-based walking trips is the 2012 California Household Travel Survey. This model finds that WalkScore is predictive of walking trips, that residential density and 4-way intersections have an additional but small impact, and that regional access by the transit network has a synergistic effect on walking, but regional access by auto has no impact when controlling for regional access by transit. </p><p> The second topic engages with the question of the impact of accessibility to local amenities on home values. Although early research has found that the composite index WalkScore is positively correlated with home values, this dissertation unpacks the impact of each category of destination used in WalkScore (as well as several others) on home values. The model shows that some amenities are far more predictive of home values in the datasets used here; in particular, cafes and coffee shops tend to be the indicator of neighborhood-scale urban fabric that has the largest positive relationship with home values, where a one standard deviation increase in access to cafes is associated with a 15\% increase in home values. </p><p> Although the previous topic provides some evidence that walkable amenities are related to increased home values with the datasets analyzed here, it does not prove that households are valuing walking to these amenities; it is equally plausible that households are capitalizing short driving trips into increased home values. The third topic thus creates a nested mode-destination model for each trip purpose (with destinations nested into modes) so that the logsums of the lower nest give an absolute measure of the accessibility by mode for each purpose for each location in the region. </p><p> These logsums are then weighted by the number of trips made for each purpose, and segmented by income and weighted by the incomes of the people that live at each location in the city. The result is an index based only on empirically observed behavior (in this case, the primary dataset is the 2012 CHTS) which is an absolute measure of walking behavior, not just of walkability. The methodology from this chapter yields an index for all three modes, and all indexes are included in the hedonic model described above. The model shows that a one standard deviation change in the auto index has the largest impact on home values, but that the walking index is positive, statistically significant, and almost as large. Although part of the reason for this finding might be that these neighborhoods are undersupplied, where they exist they are clearly in high demand. (Abstract shortened by UMI.)</p>

The research analyzes the operating performance of three commercially available curbside automated pedestrian detectors (APDs) (infrared and stereovision, passive infrared, and a microwave detector) for the actuation of pedestrian walk phases as a function of winter weather and temperature variations at signalized intersections in terms of detector selectivity and sensitivity. Two sites were selected for field analysis in Winnipeg, Manitoba Canada. Based on a sample of 8,225 detections at the two sites, the research found that overall sensitivity rates of the APDs ranged from 62 to 98 percent while selectivity rates were generally below 50 percent. Regardless of site, the infrared/video APD had the second highest sensitivity and highest selectivity rates of all APDs analyzed. The infrared APD had the highest sensitivity and lowest selectivity rates, and the microwave APD had the lowest sensitivity and second highest selectivity.

<p> Highways have become a symbol of modern America (Levinson, 2004), and infrastructure investment plays a pivotal role both in short-term and long-term economic growth and in job creation. In the US, it represents 16% of the gross national product, and every dollar of public investment in highways has a net rate of return of 22 cents, and every billion dollars of federal highway investment generates 47,500 jobs (AASHTO 2003). In response to the inabilities to raise government revenues in the US, aging infrastructure systems, and high construction and O/M costs, infrastructure development has steadily become a collaboration work between the public and private sector. In liberalized infrastructure markets, various governance structures are being tested for application of public-private partnerships (PPPs or P3s) strategies in infrastructure development (Estache, 2004). </p><p> This thesis aims to review the key complex PPP issues in transportation renewal projects in the US that adopt PPPs. While PPPs can be applied to a range of agreements, the PPP projects to be studied and analyzed in this paper will be limited to those involving complex financing, design, construction and long-term operation and maintenance of transportation infrastructure of at least 10 years. These issues are examined in the context of six case studies in six different state across the US by means of interview and archival record. Findings resulting from this work suggested that PPPs have been increasingly implemented by departments of transportation in the US as a mean to tape into private resources. In addition, this research identified four key complex PPP issues in transportation projects as such Economic issue, Procurement issue, Risk Issue, and Governance issue. States have established a dedicated organizational unit to facilitate the use of PPPs, for example High Performance Enterprise (HPTE) in Colorado and Innovative Project Delivery Division in Virginia, but there exist no standards or best practices in the United States for procurement, concession terms, or risk-sharing.</p>

<p> Existing railroad crossing crash prediction and hazard index equations are analyzed and found to inadequately measure safety at light rail crossings. The operational characteristics of common carrier freight and commuter railroads are different enough from the operational characteristics of light rail to affect the ability of existing railroad equations to accurately predict the number of crashes that occur at light rail crossings. These operational differences require light rail specific crash prediction equations to better predict crash numbers at light rail crossings. The goal of this research is to develop a method to measure safety at light rail crossings. </p><p> Through review of the literature describing different statistical methodologies that have been used to develop railroad crossing crash prediction and hazard index equations, the use of a nonlinear regression method to predict initial crash values with an Empirical Bayes Method adjustment to account for the actual crash history at the light crossing is determined to be the optimum model development method. </p><p> Operational alignment and configuration of light rail crossings are analyzed, and each is found to have some effect on the prediction of the number of crashes that occur at light rail crossings in addition to light rail vehicle volume, motor vehicle volume, sight obstructions, presence of a residential area near the light rail crossing, and the number of motor vehicle lanes crossing the crossing. Statistically valid models are developed to predict crashes based on light rail crossing alignment type, configuration type, and method of crossing control including traffic signals, flashing lights with gates, and passive signing. Sufficient data to develop a prediction equation for flashing light control is not available for this study. </p><p> The use of Geographic Information Systems (GIS) models is determined to be a benefit in use of application of the light rail specific crash number prediction equations. GIS models can be used not only to predict the number of crashes expected to occur at a light rail crossing, but also can be used to identify and analyze light rail crossing crash trends.</p>

There is no abstract available for this dissertation.

<p> Nitrogen-based fertilizer industry in United States is undergoing major changes the demand for which is primarily driven by agriculture. Traditionally, this industry sources anhydrous ammonia through imports from Canada and U.S.-Gulf, the latter comprises bulk of imports, or produces domestically to be supplied as is or converted into urea or UAN variations of nitrogen-based fertilizer with various combinations with other minerals. </p><p> With change in composition of crops and increasing acreage of crops that are fertilizer intensive, there is an increased demand for nitrogen-based fertilizer in order to promote foliar growth as a standalone form, for example Urea, or in combination, for example Di-ammonium phosphate (DAP). Second compelling reason for change in industry is reduction in prices of natural-gas, in part due to oil exploration, that makes it cheaper to produce anhydrous ammonia domestically. Anhydrous ammonia is perquisite for making other types of nitrogen-based fertilizer and highly energy intensive. Thus, lower natural-gas prices provide incentive for domestic firms to either expand existing fertilizer plants or opens up the possibility of new entrants. Many companies/firms have recently announced their plans to expand existing plants or open new units, exerting competitive pressure on an industry that already has lot of surplus capacity but highly competitive in terms of production costs and technology used. It is to be noted that natural-gas prices are volatile; therefore, any commitment to expand or open new plant is subject to volatility in demand, natural-gas prices, and import price of fertilizers. </p><p> The purpose of this dissertation is to analyze spatial competition among U.S. nitrogen-based fertilizer plants and their respective market boundaries. This dissertation also derives the structure of the supply chain for nitrogen-based fertilizer in the United States (at macro level); and the stochastic spatial-optimization model to account for risk in random variables. Locational information is used to account for spatial nature of problem, and linear and mixed-integer based optimization techniques are applied to arrive at current and most likely future cases. </p><p> Combination of linear optimization, and mixed-integer, and geographical information systems helps in determining regional areas where competition is expected to be ruinous and most intense; and provide insights on viability of newly announced fertilizer plants that are most likely to be successful and significantly impact the structure of overall supply chain. </p>

The East Side of Lake Winnipeg, North Western Ontario, and the Kivalliq region in Central Nunavut are selected as case regions in which the cost competitiveness of the cargo airship can be estimated. Data from the North West Company that describes freight movements and associated costs are used in this comparative analysis. A cargo airship developer provided operating cost data that are operationalized using the North West Company’s data. Results show that using a cargo airship could produce annual transportation cost savings of between 12.5% and 38.3% per year. These results are similar across all three regions and vary based on the scenario modelled within each case region. The findings from this research are subject to the assumptions that the operating cost model described by the cargo airship developer is accurate, and is limited in scope because it focuses solely on one company’s shipping needs in select regions.

<p>Crash-prone drivers should be effectively targeted for various safety education and regulation programs because their involvement in crashes presents a big adverse effect on highway safety. To improvement highway safety from the key element of transportation system, this research investigates crash-prone driver problem in Louisiana. Through analyzing the crash data of seven years from LADOTD, this study identifies the gravity of the problem and characteristics of crash-prone drivers and crashes they committed. </p><p> The analysis results show that quite a few drivers repeatedly had crashes; seven drivers had 13 crashes in seven years; and the maximum number of crashes occurring in a single year to a single driver is eight. Actually, the 5% of drivers who had multiple crashes were responsible for 35% of the crashes that occurred in the seven years in Louisiana. Crash injury rate is also higher for drivers with multiple crashes. The results demonstrate that young male age 15 to 24 and old female age 65 to 74 have the highest recurring crash potential, and male crash-prone drivers are generally more likely to involve in more crashes than female crash-prone drivers. The probability of having crashes in any given year is closely related to a driver&rsquo;s crash history; The results of three probability matrix models give quantitative estimate on crash risk predict at three time interval modeling scenarios. The developed probability predicting model can be used for estimating an individual future crash risk. </p><p> Based on the results, several suggestions are made on how to improve roadway safety through reducing crashes committed by drivers with much higher crash risk as identified by the analysis. </p>