A Systematic Literature Review of Drone Utility in Railway Condition Monitoring

Raj Bridgelall is the program director for the Upper Great Plains Transportation Institute (UGPTI) Center for Surface Mobility Applications & Real-time Simulation environments (SMARTSeSM).Drones have recently become a new tool in railway inspection and monitoring (RIM) worldwide, but there is still a lack of information about the specific benefits and costs. This study conducts a systematic literature review (SLR) of the applications, opportunities, and challenges of using drones for RIM. The SLR technique yielded 47 articles filtered from 7,900 publications from 2014 to 2022. The SLR found that key motivations for using drones in RIM are to reduce costs, improve safety, save time, improve mobility, increase flexibility, and enhance reliability. Nearly all the applications fit into the categories of defect identification, situation assessment, rail network mapping, infrastructure asset monitoring, track condition monitoring, and obstruction detection. The authors assessed the open technical, safety, and regulatory challenges. The authors also contributed a cost analysis framework, identified factors that affect drone performance in RIM, and offered implications for new theories, management, and impacts to society.The authors conducted this work with support from North Dakota State University and the Mountain-Plains Consortium, a University Transportation Center funded by the U.S. Department of Transportation.https://www.ugpti.org/about/staff/viewbio.php?id=7

Introducing an Efficiency Index to Evaluate eVTOL Designs

Raj Bridgelall is the program director for the Upper Great Plains Transportation Institute (UGPTI) Center for Surface Mobility Applications & Real-time Simulation environments (SMARTSeSM).The evolution of electric vertical takeoff and landing (eVTOL) aircraft as part of the Advanced Air Mobility initiative will affect our society and the environment in fundamental ways. Technological forecasting suggests that commercial services are fast emerging to transform urban and regional air mobility for people and cargo. However, the complexities of diverse design choices pose a challenge for potential adopters or service providers because there are no objective and simple means to compare designs based on the available set of performance specifications. This analysis defines an aeronautically informed propulsion efficiency index (PEX) to compare the performance of eVTOL designs. Range, payload ratio, and aspect ratio are the minimum set of independent parameters needed to compute a PEX that can distinguish among eVTOL designs. The distribution of the PEX and the range are lognormal in the design space. There is no association between PEX values and the mainstream eVTOL architecture types or the aircraft weight class. A multilinear regression showed that the three independent parameters explained more than 90% of the PEX distribution in the present design space.https://www.ugpti.org/about/staff/viewbio.php?id=7

Hyperspectral Applications in the Global Transportation Infrastructure

Raj Bridgelall is the program director for the Upper Great Plains Transportation Institute (UGPTI) Center for Surface Mobility Applications & Real-time Simulation environments (SMARTSeSM).Hyperspectral remote sensing is an emerging field with potential applications in the observation, management, and maintenance of the global transportation infrastructure. This study introduces a general analytical framework to link transportation systems analysis and hyperspectral analysis. The authors introduce a range of applications that would benefit from the capabilities of hyperspectral remote sensing. They selected three critical but unrelated applications and identified both the spatial and spectral information of their key operational characteristics to demonstrate the hyperspectral utility. The specific scenario studies exemplifies the general approach of utilizing the outputs of hyperspectral analysis to improve models that practitioners currently use to analyze a variety of transportation problems including roadway congestion forecasting, railway condition monitoring, and pipeline risk management.Mountain Plains Consortium (MPC)https://www.ugpti.org/about/staff/viewbio.php?id=7

North Dakota Strategic Freight Analysis: Item IV. Heavier Loading Rail Cars

North Dakota's grain producers rely on an efficient rail system to move their products to export and domestic markets. A recent shift to larger grain hopper cars may threaten the viability of the state's light-density branch line network. This study simulates the impacts of handling larger rail cars on many types of rail lines, model the decision process used by railroads in deciding whether to upgrade such lines or abandon them, estimates the costs of upgrading rail lines that are unlikely to be upgraded, and estimates generalized highway impacts that could result from the abandonment of non-upgraded lines

North Dakota Strategic Freight Analysis Agricultural Sector: Summary Report

In an attempt to provide some of the information that will enable North Dakota firms and policymakers to make better decisions, this project addressed four transportation issues, which are critical to the future of the state's agricultural sector: (1) the impact of 110-car shuttle trains on the marketing of grains, (2) the impact of heavier cars on light-density rail lines, (3) the changing trend in the use of truck/rail container intermodal transportation for marketing North Dakota products; and (4) the role played by logistics factors in determining the optimal location of value-added facilities

Accuracy Enhancement of Roadway Anomaly Localization Using Connected Vehicles

Raj Bridgelall is the program director for the Upper Great Plains Transportation Institute (UGPTI) Center for Surface Mobility Applications & Real-time Simulation environments (SMARTSeSM).The timely identification and localization of roadway anomalies that pose hazards to the traveling public is currently a critical but very expensive task. Hence, transportation agencies are evaluating emerging alternatives that use connected vehicles to lower the cost dramatically and to increase simultaneously both the monitoring frequency and the network coverage. Connected vehicle methods use conventional GPS receivers to tag the inertial data stream with geospatial position estimates. In addition to the anticipated GPS trilateration errors, numerous other factors reduce the accuracy of anomaly localization. However, practitioners currently lack information about their characteristics and significance. This study developed error models to characterize the factors in position biases so that practitioners can estimate and remove them. The field studies revealed the typical and relative contributions of each factor, and validated the models by demonstrating agreement of their statistics with the anticipated norms. The results revealed a surprising potential for tagging errors from embedded systems latencies to exceed the typical GPS errors and become dominant at highway speeds.University Transportation CentreU.S. Department of Transportation (USDOT)Mountain Plains Consortium (MPC)Grant DTRT12-G-UTC08https://www.ugpti.org/about/staff/viewbio.php?id=7

Characterizing Pavement Roughness at Non-Uniform Speeds Using Connected Vehicles

Raj Bridgelall is the program director for the Upper Great Plains Transportation Institute (UGPTI) Center for Surface Mobility Applications & Real-time Simulation environments (SMARTSeSM).Methods of pavement roughness characterizations using connected vehicles are poised to scale beyond the frequency, span, and affordability of existing methods that require specially instrumented vehicles and skilled technicians. However, speed variability and differences in suspension behavior require segmentation of the connected vehicle data to achieve some level of desired precision and accuracy with relatively few measurements. This study evaluates the reliability of a Road Impact Factor (RIF) transform under stop-and-go conditions. A RIF-transform converts inertial signals from on-board accelerometers and speed sensors to roughness indices (RIF-indices), in real-time. The case studies collected data from 18 different buses during their normal operation in a small urban city. Within 30 measurements, the RIF-indices distributed normally with an average margin-of-error below 6%. This result indicates that a large number of measurements will provide a reliable estimate of the average roughness experienced. Statistical t-tests distinguished the relatively small differences in average roughness levels among the roadway segments evaluated. In conclusion, when averaging roughness measurements from the same type of vehicle moving at non-uniform speeds, the RIF-transform will provide everincreasing precision and accuracy as the traversal volume increases.Upper Great Plains Transportation Institute (UGPTI)National Center for Transit Research (NCTR)Small Urban and Rural Transit Center (SURTC) of the Upper Great Plains Transportation Institute (UGPTI)https://www.ugpti.org/about/staff/viewbio.php?id=7

Railroad Accident Analysis Using Extreme Gradient Boosting

Raj Bridgelall is the program director for the Upper Great Plains Transportation Institute (UGPTI) Center for Surface Mobility Applications & Real-time Simulation environments (SMARTSeSM).Railroads are critical to the economic health of a nation. Unfortunately, railroads lose hundreds of millions of dollars from accidents each year. Trends reveal that derailments consistently account for more than 70% of the U.S. railroad industry?s average annual accident cost. Hence, knowledge of explanatory factors that distinguish derailments from other accident types can inform more cost-effective and impactful railroad risk management strategies. Five feature scoring methods, including ANOVA and Gini, agreed that the top four explanatory factors in accident type prediction were track class, type of movement authority, excess speed, and territory signalization. Among 11 different types of machine learning algorithms, the extreme gradient boosting method was most effective at predicting the accident type with an area under the receiver operating curve (AUC) metric of 89%. Principle component analysis revealed that relative to other accident types, derailments were more strongly associated with lower track classes, non-signalized territories, and movement authorizations within restricted limits. On average, derailments occurred at 16 kph below the speed limit for the track class whereas other accident types occurred at 32 kph below the speed limit. Railroads can use the integrated data preparation, machine learning, and feature ranking framework presented to gain additional insights for managing risk, based on their unique operating environments.North Dakota State Universityhttps://www.ugpti.org/about/staff/viewbio.php?id=7