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Subject: Transportation.

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Now showing 1 - 10 of 26
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    Precision Enhancement of Pavement Roughness Localization with Connected Vehicles
    (2016) Bridgelall, Raj; Huang, Y.; Zhang, Z.; Deng, F.; Upper Great Plains Transportation Institute
    Transportation agencies rely on the accurate localization and reporting of roadway anomalies that could pose serious hazards to the traveling public. However, the cost and technical limitations of present methods prevent their scaling to all roadways. Connected vehicles with on-board accelerometers and conventional geospatial position receivers offer an attractive alternative because of their potential to monitor all roadways in real-time. The conventional global positioning system is ubiquitous and essentially free to use but it produces impractically large position errors. This study evaluated the improvement in precision achievable by augmenting the conventional geofence system with a standard speed bump or an existing anomaly at a pre-determined position to establish a reference inertial marker. The speed sensor subsequently generates position tags for the remaining inertial samples by computing their path distances relative to the reference position. The error model and a case study using smartphones to emulate connected vehicles revealed that the precision in localization improves from tens of metres to sub-centimetre levels, and the accuracy of measuring localized roughness more than doubles. The research results demonstrate that transportation agencies will benefit from using the connected vehicle method to achieve precision and accuracy levels that are comparable to existing laser-based inertial profilers.
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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.
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    Strategic Global Logistics Management for Sourcing Road Oil in the U.S.
    (2017) Bridgelall, Raj; Lee, EunSu; Bell, Michael; Upper Great Plains Transportation Institute
    The demand for asphalt and road oil heavily leverages local supply because the product is a hot binder of aggregates that form the final mix needed to pave roads. This paper discusses the supply chain characteristics of crude oil feedstock by considering the overall logistics of sourcing heavy crude oil domestically, or importing it from international trading partners. Heavy crude oil is a source of asphalt and road oil production. The study examines critical global and domestic logistics factors such as customs, regulations, security, environmental compliance, and natural events that will affect costs, schedules, and risks. The study provides a framework for decision-making in sourcing the feedstock. The study helps global logisticians and transportation managers improve strategic design and planning towards efficient sourcing.
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    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.
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    Energy Efficiency of CSMA Protocols for Wireless Packet Switched Networks
    (2004) Bridgelall, Raj; Chan, Douglas S.; Berger, Toby; Upper Great Plains Transportation Institute
    The finite battery power in wireless portable computing devices is a motivating factor for developing energy efficient wireless network technologies. This paper investigates energy efficiency, relating it to throughput and packet delay for both non-persistent and p-persistent CSMA, two protocols popularly applied in current wireless networks; for example, the widely adopted IEEE 802.11 WLAN standards are based on p-persistent CSMA. For high message generation by the members of a finite population, we find that non-persistent CSMA is optimized for energy efficiency, throughput and delay are impacted negatively, whereas p-persistent CSMA can effectively optimize all three with the same network settings. Our results help illuminate the suitability of each CSMA scheme for various wireless environments and applications.
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    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.
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    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.
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    Inertial Sensor Sample Rate Selection for Ride Quality Measures
    (2014) Bridgelall, Raj; Upper Great Plains Transportation Institute
    The Road Impact Factor is a measure of ride-quality. It is derived from the average inertial response of vehicles to road roughness. Unlike the International Roughness Index, the most common measure, the road impact factor does not rely on specialized instrumentation to measure spatial deviations from a flat profile. The most significant advantage of the Road Impact Factor is that low-cost sensors distributed in smartphones and connected vehicles generate the measurements directly. Standardizing the sample rate of inertial sensors in vehicles will provide consistent measures at any speed. This study characterizes the impact of sample rate and traversal volume on measurement consistency, and conducts case studies to validate the theories developed for a recommended standard at 64 hertz.
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    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.
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    Pavement Performance Evaluations Using Connected Vehicles
    (2016) Bridgelall, Raj; Upper Great Plains Transportation Institute
    The ability of any nation to support economic growth and commerce relies on their capacity to preserve and to sustain the performance of pavement assets. The ever-widening funding gap to maintain pavements challenges the scaling of existing techniques to measure ride quality. The international roughness index is the primary indicator used to assess and forecast maintenance needs. Its fixed simulation procedure has the advantage of requiring relatively few traversals to produce a consistent characterization. However, the procedure also underrepresents roughness that riders experience from spatial wavelengths that fall outside of the model’s sensitivity range. This paper introduces a connected vehicle method that fuses inertial and geospatial position data from many vehicles to expose roughness experienced from all spatial wavelengths. This study produced both roughness indices simultaneously from the same inertial profiler. The statistical distribution of their ratios agreed with a classic t-distribution. The two indices collected from three different pavement sections at two different speeds exhibit a direct proportionality within a margin-of-error that diminished below 2% as the extrapolated traversal volume approached 100. Practitioners are currently evaluating the connected vehicle method to implement lower-cost and more scalable alternatives to the international roughness index.