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Rolling-Stock Automatic In-Situ Line Deterioration and Operating Condition Sensing
(2013) Bridgelall, Raj; Lu, Pan; Tolliver, Denver D.; Upper Great Plains Transportation Institute
Track and equipment failures dominate railroad accident causes. Railroads must visually inspect most tracks in service as often as twice weekly to comply with the Federal Track Safety Standards. They augment visual inspections with automated non-destructive-evaluation (NDE) equipment to locate developing and mature defects. However, the defect formation rate is escalating with increasing traffic load density and continuously declining railroad employment per track-mile. This indicates a widening gap between the rate of defect formation and the resources available to find them before they result in accidents, delays, and lost revenue. With resources thinly stretched and the rate of defect formation escalating with traffic load-density, railroads are seeking to enhance the efficiency of inspections and maintenance of way. This paper describes the development of a Rolling-stock Automatic In-situ Line Deterioration & Operating Condition Sensing (RAILDOCS) system to automatically locate and classify track and rail vehicle defects. The approach incorporates a new low-cost wireless sensor technology and Cloud computing method to guide and focus inspection activities to locations of equipment and track defect symptoms, leading to efficient diagnosis and remediation. RAILDOCS has on-board sensors which will continuously monitor track and vehicle condition and transmit a 3D inertial signature for a remote processor to analyze and produce a complete and updated picture of aggregate track and equipment quality. RAILDOCS complement more expensive visual and NDE methods by reallocating time spent on defect discovery to detailed inspections of prioritized defect symptom locations. Symptom sensors integrate micro-electro-mechanical (MEMS), global positioning system (GPS) satellite receivers, wireless communications, and microprocessors technology. Cloud computing and signal processing algorithms produce a track quality index, and forecast optimum maintenance triggers.
Introducing an Efficiency Index to Evaluate eVTOL Designs
(2023) Bridgelall, Raj; Askarzadeh, Taraneh; Tolliver, Denver D.; Upper Great Plains Transportation Institute
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.
Use of Connected Vehicles to Characterize Ride Quality
(2016) Bridgelall, Raj; Rahman, Md Tahmidur; Tolliver, Denver D.; Daleiden, Jerome F.; Upper Great Plains Transportation Institute
The United States rely on the performance of more than four 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 utilize a laser-based inertial profiler to produce the index. However, technical, practical, and budget constraints preclude their use for some facilities, particularly local and unpaved roads that make up more than 90% of the road network in the US. 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.
Accuracy Enhancement of Anomaly Localization with Participatory Sensing Vehicles
(2020) Bridgelall, Raj; Tolliver, Denver D.; Upper Great Plains Transportation Institute
Transportation agencies cannot afford to scale existing methods of roadway and railway condition monitoring to more frequently detect, localize, and fix anomalies throughout networks. Consequently, anomalies such as potholes and cracks develop between maintenance cycles and cause severe vehicle damage and safety issues. The need for a lower-cost and more-scalable solution spurred the idea of using sensors on board vehicles for a continuous and network-wide monitoring approach. However, the timing of the full adoption of connected vehicles is uncertain. Therefore, researchers used smartphones to evaluate a variety of methods to implement the application using regular vehicles. However, the poor accuracy of standard positioning services with low-cost geospatial positioning system (GPS) receivers presents a significant challenge. The experiments conducted in this research found that the error spread can exceed 32 m, and the mean localization error can exceed 27 m at highway speeds. Such large errors can make the application impractical for widespread use. This work used statistical techniques to inform a model that can provide more accurate localization. The proposed method can achieve sub-meter accuracy from participatory vehicle sensors by knowing only the mean GPS update rate, the mean traversal speed, and the mean latency of tagging accelerometer samples with GPS coordinates.
Characterizing Pavement Roughness at Non-Uniform Speeds Using Connected Vehicles
(2017) Bridgelall, Raj; Hough, Jill; Tolliver, Denver D.; Upper Great Plains Transportation Institute
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.
Policy Implications of Truck Platooning and Electrification
(2020) Bridgelall, Raj; Patterson, Douglas A.; Tolliver, Denver D.; Upper Great Plains Transportation Institute
Trucks in North America account for more than 23% of the transportation sector’s greenhouse gas emissions. Truck platooning and truck electrification are potential technologies for reducing emissions and operating cost. However, adoption uncertainties result in speculations about their potential impact. Traditional modeling techniques to inform policymaking use large datasets, trained professionals to calibrate complex software, and take hours to run a single scenario. This paper provides a closed-form model that rapidly calculates trends of the potential national petroleum consumption reduction for a range of technology adoption scenarios. The primary finding is that truck electrification would have a substantially larger impact on fuel consumption reduction than platooning. The limitations of platoonable miles create an upper bound in benefits. When calibrated for the base year fuel-efficiency, the model shows that petroleum consumption reduction would be less than 4% at full adoption of platooning. The electrification of single unit trucks results in more than a 13-fold reduction of national petroleum consumption relative to platooning. However, without the electrification of combination unit trucks, petroleum consumption will eventually begin to increase again. Therefore, policies to encourage the reduction of greenhouse gas emissions should not overlook incentives to electrify combination unit trucks.
Hyperspectral Range Imaging for Transportation Systems Evaluation
(2016) Bridgelall, Raj; Rafert, J. Bruce; Tolliver, Denver D.; Atwood, Don; Upper Great Plains Transportation Institute
Transportation agencies expend significant resources to inspect critical infrastructure such as roadways, railways, and pipelines. Regular inspections identify important defects and generate data to forecast maintenance needs. However, cost and practical limitations prevent the scaling of current inspection methods beyond relatively small portions of the network. Consequently, existing approaches fail to discover many high-risk defect formations. Remote sensing techniques offer the potential for more rapid and extensive non-destructive evaluations of the multimodal transportation infrastructure. However, optical occlusions and limitations in the spatial resolution of typical airborne and spaceborne platforms limit their applicability. This research proposes hyperspectral image classification to isolate transportation infrastructure targets for high-resolution photogrammetric analysis. A plenoptic swarm of unmanned aircraft systems will capture images with centimeter-scale spatial resolution, large swaths, and polarization diversity. The light field solution will incorporate structure-from-motion techniques to reconstruct three-dimensional details of the isolated targets from sequences of two-dimensional images. A comparative analysis of existing low-power wireless communications standards suggests an application dependent tradeoff in selecting the best-suited link to coordinate swarming operations. This study further produced a taxonomy of specific roadway and railway defects, distress symptoms, and other anomalies that the proposed plenoptic swarm sensing system would identify and characterize to estimate risk levels.
Resolution Agile Remote Sensing for Detection of Hazardous Material Spills
(2016) Bridgelall, Raj; Rafert, James B.; Tolliver, Denver D.; Lee, EunSu; Upper Great Plains Transportation Institute
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. The early detection of spills will potentially save lives, protect the environment, and thwart the need for expensive clean up 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 lack 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 fieldof- 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.
Rapid Hyperspectral Image Classification to Enable Autonomous Search Systems
(2016) Bridgelall, Raj; Rafert, J. Bruce; Tolliver, Denver D.; Lee, EunSu; Upper Great Plains Transportation Institute
The emergence of lightweight full-frame hyperspectral cameras is destined to enable autonomous search vehicles in the air, on the ground, and in water. Self-contained and long-endurance systems will yield important new applications, for example, in emergency response and the timely identification of environmental hazards. One missing capability is rapid classification of hyperspectral scenes so that search vehicles can immediately take actions to verify potential targets. Onsite verifications minimize false positives and preclude the expense of repeat missions. Verifications will require enhanced image quality, which is achievable by either moving closer to the potential target or by adjusting the optical system. Such a solution, however, is currently impractical for small mobile platforms with finite energy sources. Rapid classifications with current methods demand large computing capacity that will quickly deplete the on-board battery or fuel. To develop the missing capability, the authors propose a low-complexity hyperspectral image classifier that approaches the performance of prevalent classifiers. This research determines that the new method will require at least 19-fold less computing capacity than the prevalent classifier. To assess relative performances, the authors developed a benchmark that compares a statistic of library endmember separability in their respective feature spaces.
Budgeting the Adoption of Sensors on Connected Trains
(2021) Bridgelall, Raj; Tolliver, Denver D.; Upper Great Plains Transportation Institute
Railroads can save millions of dollars by deploying multi-sensor track scanners on connected trains to detect track and roadbed problems that could cause accidents. However, uncertainties about performance and return-on-investment impeded the development and deployment of such sensor systems. This research develops a budget model that both manufacturers and railroads can use to decide on a suitable tradeoff between price affordability and achievable performance. A case study of five Class 1 railroads demonstrates that a payback within two years is achievable at $4,000 per device and an annual maintenance cost of one-quarter the system deployment cost.

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