Browsing by Author "Tolliver, Denver D."

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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.
Accuracy Enhancement of Roadway Anomaly Localization Using Connected Vehicles
(2016) Bridgelall, Raj; Tolliver, Denver D.; Upper Great Plains Transportation Institute
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.
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.
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.
Closed Form Models to Assess Railroad Technology Investments
(2020) Bridgelall, Raj; Tolliver, Denver D.; Upper Great Plains Transportation Institute
Class I railroads in North America collectively invested $11.2 billion to comply with a federal mandate to deploy positive train control. This amount dwarfs the potential savings from accidents the technology could prevent. Therefore, railroads must seek additional benefits. This research contributes simple closed-form models to inform strategies that can leverage the technology deployment by estimating the annual additional net benefits, internal rate of return, and benefit-cost ratio needed for a desired payback period.
A Cognitive Framework to Plan for the Future of Transportation
(2020) Bridgelall, Raj; Tolliver, Denver D.; Upper Great Plains Transportation Institute
Automated, connected, electrified, and shared mobility will be cornerstones of the transportation future. Research to quantify the potential benefits and drawbacks of practice, and to identify barriers to adoption is the first step in any strategic plan for their adoption. However, uncertainties, complexity, interdependence, and the multidisciplinary nature of emerging transportation technologies make it difficult to organize and identify focused research. The contribution of this work is a cognitive framework to help planners and policy-makers organize broad topics, reveal challenges, discover ideas for solutions, quantify potential impacts, and identify implications to guide preparation strategies. The authors provide example cognitive frameworks for connected, automated, and electrified vehicles.
Detecting Sources of Ride Roughness by Ensemble Connected Vehicle Signals
(2022) Bridgelall, Raj; Bhardwaj, Bhavana; Lu, Pan; Tolliver, Denver D.; Dhingra, Neeraj; Upper Great Plains Transportation Institute
It is expensive and impractical to scale existing methods of road condition monitoring for more frequent and network-wide coverage. Consequently, defects that increase ride roughness or can cause accidents will go undetected. This paper presents a method to enable network-wide, continuous monitoring by using low-cost GPS receivers and accelerometers on board regular vehicles. The technique leverages the large volume of sensor signals from multiple traversals of a road segment to enhance the signal quality by ensemble averaging. However, ensemble averaging requires position-repeatable signals which is not possible because of the low resolution and low accuracy of GPS receivers and the non-uniform sampling of accelerometers. This research overcame those challenges by integrating methods of interpolation, signal resampling, and correlation alignment. The experiments showed that the approach doubled the peak of the composite signal by decreasing signal misalignment by a factor of 67. The signal-to-noise ratio increased by 10 dBs after combining the signals from only 6 traversals. A probabilistic model developed to estimate a dynamic signal-detection threshold demonstrated that both the false-positive and false-negative rates approached zero after combining the signals from 15 traversals. The method will augment the efficiency of follow-up inspections by focusing resources to locations that consistently produce rough rides.
Enhancement of Signals from Connected Vehicles to Detect Roadway and Railway Anomalies
(2019) Bridgelall, Raj; Chia, Leonard; Bhardwaj, Bhavana; Lu, Pan; Tolliver, Denver D.; Dhingra, Neeraj; Upper Great Plains Transportation Institute
Frequent network-wide monitoring of the condition of roadways and railways prevent fatalities, injuries, and financial losses. Even so, agencies cannot afford to inspect vast transportation networks using present methods. Therefore, the idea of using low-cost sensors aboard connected vehicles became appealing. However, low-cost sensors introduce new challenges to improve poor signal quality which causes detection errors. Common approaches apply computationally complex filters to individual signal streams, which limits further improvements. This paper presents a method that combines signals from each traversal in a manner that leads to ever-increasing signal quality. The proposed method addresses the challenges of poor accuracy and precision of position estimates from global positioning system (GPS) receivers, and errors from the non-uniform sampling of low-cost accelerometers. The result is improved signal quality from a 20% improvement in signal alignment over GPS and a 90-fold enhancement in distance precision.
Error Sensitivity of the Connected Vehicle Approach to Pavement Performance Evaluations
(2016) Bridgelall, Raj; Rahman, Md Tahmidur; Tolliver, Denver D.; Daleiden, Jerome F.; Upper Great Plains Transportation Institute
The international roughness index is the prevalent indicator used to assess and forecast road maintenance needs. The fixed parameters of its simulation model provide the advantage of requiring relatively few traversals to produce a consistent index. However, the static parameters also cause the model to under-represent roughness that riders experience from profile wavelengths outside of the model’s response range. A connected vehicle method that uses a similar but different index to characterize roughness can do so by accounting for all vibration wavelengths that the actual vehicles experience. This study characterizes and compares the precision of each method. The field studies indicate that within 7 traversals, the connected vehicle approach could achieve the same level of precision as the procedure used to produce the international roughness index. For a given vehicle and segment lengths longer than 50 meters, the margin-of-error diminished below 1.5% after 50 traversals, and continued to improve further as the traversal volume grew. Practitioners developing new tools to evaluate pavement performance will benefit from this study by understanding the precision trade-off to recommend best practices in utilizing the connected vehicle method.
Hyperspectral Applications in the Global Transportation Infrastructure
(2015) Bridgelall, Raj; Rafert, J. Bruce; Tolliver, Denver D.; Upper Great Plains Transportation Institute
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.
Hyperspectral Imaging Utility for Transportation Systems
(2015) Bridgelall, Raj; Rafert, J. Bruce; Tolliver, Denver D.; Upper Great Plains Transportation Institute
The global transportation system is massive, open, and dynamic. Existing performance and condition assessments of the complex interacting networks of roadways, bridges, railroads, pipelines, waterways, airways, and intermodal ports are expensive. Hyperspectral imaging is an emerging remote sensing technique for the non-destructive evaluation of multimodal transportation infrastructure. Unlike panchromatic, color, and infrared imaging, each layer of a hyperspectral image pixel records reflectance intensity from one of dozens or hundreds of relatively narrow wavelength bands that span a broad range of the electromagnetic spectrum. Hence, every pixel of a hyperspectral scene provides a unique spectral signature that offers new opportunities for informed decision-making in transportation systems development, operations, and maintenance. Spaceborne systems capture images of vast areas in a short period but provide lower spatial resolution than airborne systems. Practitioners use manned aircraft to achieve higher spatial and spectral resolution, but at the price of custom missions and narrow focus. The rapid size and cost reduction of unmanned aircraft systems promise a third alternative that offers hybrid benefits at affordable prices by conducting multiple parallel missions. This research formulates a theoretical framework for a pushbroom type of hyperspectral imaging system on each type of data acquisition platform. The study then applies the framework to assess the relative potential utility of hyperspectral imaging for previously proposed remote sensing applications in transportation. The authors also introduce and suggest new potential applications of hyperspectral imaging in transportation asset management, network performance evaluation, and risk assessments to enable effective and objective decision- and policy-making.
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.
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.
Mining Connected Vehicle Data for Beneficial Patterns in Dubai Taxi Operations
(2018) Bridgelall, Raj; Lu, Pan; Tolliver, Denver D.; Xu, Tie; Upper Great Plains Transportation Institute
On-demand shared mobility services such as Uber and micro-transit are steadily penetrating the worldwide market for traditional dispatched taxi services. Hence, taxi companies are seeking ways to compete. This study mined large-scale mobility data from connected taxis to discover beneficial patterns that may inform strategies to improve dispatch taxi business. It is not practical to manually clean and filter large-scale mobility data that contains GPS information. Therefore, this research contributes and demonstrates an automated method of data cleaning and filtering that is suitable for such types of datasets. The cleaning method defines three filter variables and applies a layered statistical filtering technique to eliminate outlier records that do not contribute to distributions that match expected theoretical distributions of the variables. Chi-squared statistical tests evaluate the quality of the cleaned data by comparing the distribution of the three variables with their expected distributions. The overall cleaning method removed approximately 5% of the data, which consisted of errors that were obvious and others that were poor quality outliers. Subsequently, mining the cleaned data revealed that trip production in Dubai peaks for the case when only the same two drivers operate the same taxi. This finding would not have been possible without access to proprietary data that contains unique identifiers for both drivers and taxis. Datasets that identify individual drivers are not publicly available.
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.
Railroad Accident Analysis Using Extreme Gradient Boosting
(2021) Bridgelall, Raj; Tolliver, Denver D.; Upper Great Plains Transportation Institute
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.
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.
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.
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.
A Systematic Literature Review of Drone Utility in Railway Condition Monitoring
(2023) Askarzadeh, Taraneh; Bridgelall, Raj; Tolliver, Denver D.; Upper Great Plains Transportation Institute
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.