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Railroad pricing behavior in shipping agricultural commodities from North Dakota to domestic and export destinations is examined by using an econometric technique with cross-sectional data from 1984 to 1989. North Dakota is a typical captive market where intermodal and intramodal competition is highly limited. However, intermodal and intramodal competition are significant factors that influence rail rates. The findings of the study suggest that the degree of captivity varies within the state. In addition, rail rates are very sensitive to distance, volume, and weight per car. This study also indicates that rail rates for wheat and barley are higher than those for corn and soybeans, which are produced heavily in water competitive regions. (Reprinted by permission of the publisher.), I. Introduction Railroads are important for shipping agricultural commodities from producing regions to domestic processing plants and export locations in the United States. In 1989, over 50 percent of U.S. [...]

The ongoing proliferation and diversification of remote sensing platforms offer greater flexibility to select from a range of hyperspectral imagers as payloads. The emergence of low-cost unmanned aircraft systems (drones) and their launch flexibility present an opportunity to maximize spectral resolution while scaling both daily spatial coverage and spatial resolution simultaneously by operating synchronized swarms. This article presents a model to compare the performance of hyperspectral-imaging platforms in their spatial coverage and spatial resolution envelope. The authors develop a data acquisition framework and use the model to compare the achievable performance among existing airborne and spaceborne hyperspectral imaging vehicles and drone swarms. The results show that, subject to cost and operational limitations, a platform implemented with drone swarms has the potential to provide greater spatial resolution for the same daily ground coverage compared with existing airborne platforms.

The evolving complexities of railroad systems also increase their vulnerability to failure from human error. This study compared the outcomes of two workflows that incorporated 11 different machine learning techniques to identify characteristics of railroad operations that are generally associated with human-caused accidents. The first workflow engineered features from the fixed attribute fields of a large railroad accident database and the second applied natural language processing to extract features from the unstructured accident narratives. Both workflows applied a Shapely game-theoretic model to rank the importance of features based on their marginal contribution towards predicting accident cause. Among several interesting findings, some of the most unexpected were that human-caused accidents are generally not associated with high train speeds nor derailment type accidents, and that shoving cars is riskier than pulling cars. Those, and other findings, from this study can inform management decisions, planning, and policies to minimize the risk of human-caused accidents.

Traffic carrying flammable, corrosive, poisonous, and radioactive materials continues to increase in proportion with the growth in their production and consumption. The sustained risk of accidental releases of such hazardous materials poses serious threats to public safety. Early detection of spills will potentially save lives, protect the environment, and thwart the need for expensive cleanup campaigns. Ground patrols and terrestrial sensing equipment cannot scale cost-effectively to cover the entire transportation network. Remote sensing with existing airborne and spaceborne platforms has the capacity to monitor vast areas regularly but often lacks the spatial resolution necessary for high accuracy detections. The emergence of unmanned aircraft systems with lightweight hyperspectral image sensors enables a resolution agile approach that can adapt both spatial and spectral resolutions in real time. Equipment operators can exploit such a capability to enhance the resolution of potential target materials detected within a larger field-of-view to verify their identification or to perform further inspections. However, the complexity of algorithms available to classify hyperspectral scenes limits the potential for real-time target detection to support rapid decision making. This research introduces and benchmarks the performance of a low-complexity method of hyperspectral image classification. The hybrid supervised–unsupervised technique approaches the performance of prevailing methods that are at least 30-fold more computationally complex.

The United States relies on the performance of more than 4 million miles of roadways to sustain its economic growth and to support the dynamic mobility needs of its growing population. The funding gap to build and maintain roadways is ever widening. Hence the continuous deterioration of roads from weathering and usage poses significant challenges. Transportation agencies measure ride quality as the primary indicator of roadway performance. The international roughness index is the prevalent measure of ride quality that agencies use to assess and forecast maintenance needs. Most jurisdictions use a laser-based inertial profiler to produce the index. However, technical, practical, and budget constraints preclude that use for some facility types, particularly local and unpaved roads that make up more than 90% of the road network in the United States. This study expands on previous work that developed a method to transform sensor data from many connected vehicles to characterize ride quality continuously, for all facility types and at any speed. The case studies used a certified and calibrated inertial profiler to produce the international roughness index. A smartphone aboard the inertial profiler produced simultaneously the roughness index of the connected vehicle method. The results validate the direct proportionality relationship between the inertial profiler and connected vehicle methods within a margin of error that diminished below 5% and 2% after 30 and 80 traversal samples, respectively.

Abstract:What else can airline managers do to reduce the likelihood of financial losses? The US global airline industry is characterized by highly cyclical and inconsistent operating profits, razor-thin profit margins, and unimpressive passenger yields. The objective of this research is to explore a new approach to airline seat allocation in global markets by employing a risk mitigation model, using portfolio theory to diversity an airline's route network. A portfolio of available seat miles distributed to global regions is determined using the Mean-Variance approach, followed by a second portfolio approach, the Mean-Value-at-Risk (VaR) approach. Last, a comparison is made between the two approaches in terms of actual airline operating profits. Given the financial improvements shown by the employed techniques, there is promise in pursing these methods for airline seat allocation.

The U.S. Congress mandated the development of six transportation management systems with the passage of the Intermodal Surface Transportation Efficiency Act of 1991. Each of these management systems provides transportation planners with an inventory of transportation facilities and associated performance data that can be used to relate the performance of the facility. Toward the end of achieving better transportation planning, the management systems can be further integrated to yield a powerful transportation planning tool that can be used to analyze and quantify the pavement needs of intermodal facilities. Pavement designers can use this tool for traffic forecasting to allow more representative pavement designs. Transportation planners can also use this tool to predict the results of changes in shipping practices before they occur. The described procedure greatly enhances the transportation planning process by allowing the process to be more proactive rather than reactive. Modal shifts in freight traffic can result in significantly increased highway maintenance costs. These shifts can be induced by changes in the regulatory or fiscal policies governing transportation or by physical changes in the transportation infrastructure. As an example, the abandonment of a set of branch lines is analyzed to determine the increased highway maintenance costs that would result if all shipments currently carried by rail along the branch lines were diverted to truck transportation. A background of the transportation management systems and their application to transportation planning is provided.