Transportation, Logistics, and Finance

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Browsing Transportation, Logistics, and Finance by browse.metadata.department "Civil & Environmental Engineering"

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    Calibration of Smartphone Sensors to Evaluate the Ride Quality of Paved and Unpaved Roads
    (2020) Yang, Xinyi; Hu, Liuqing; Ahmed, Hafiz Usman; Bridgelall, Raj; Huang, Ying; Upper Great Plains Transportation Institute
    Transportation agencies report that millions of crashes are caused by poor road conditions every year, which makes the localization of roadway anomalies extremely important. Common methods of road condition evaluation require special types of equipment that are usually expensive and time-consuming. Therefore, the use of smartphones has become a potential alternative. However, differences in the sensitivity of their inertial sensors and their sample rate can result in measurement inconsistencies. This study validated those inconsistencies by using three different types of smartphones to collect data from the traversal of both a paved and an unpaved road. Three calibration methods were used including the reference-mean, reference-maximum, and reference-road-type methods. Statistical testing under identical conditions of device mounting using the same vehicle revealed that the roughness indices derived from each device and road type are normally distributed with unequal means. Consequently, applying a calibration coefficient to equalize the means of the distributions of roughness indices produced from any device using the reference mean method resulted in consistent measurements for both road types.
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    Effects of smartphone sensor variability in road roughness evaluation
    (2021) Ahmed, Hafiz Usman; Hu, Liuqing; Yang, Xinyi; Bridgelall, Raj; Huang, Ying; Upper Great Plains Transportation Institute
    Accelerometers embedded in smartphones have become an alternative means of measuring the roughness of roads. However, the differences in their sensitivity and sampling rates between smartphones could produce measurement inconsistencies that challenge the wide spread of the smartphone approach for road roughness measurements. In this study, the roughness measurement inconsistency was investigated between smartphones from three different brands. Using the same vehicle, device mount method, traversal speed, and method of producing a roughness index, field experiments demonstrated that accelerometer sensitivities and maximum sample rates vary significantly among smartphones of the same brand as well as across brands. For each smartphone, to achieve a margin-of-error within a 95% of confidence, significant large amounts of traversals are needed. Specifically, 24 and 35 traversals for a paved and an unpaved road, respectively. A higher sampling rate produced more consistent measurements and the least margin-of-error but resulted in larger data sizes. In addition, the measurements from all smartphones were not very sensitive to the size of the feature extraction window, therefore, selecting the largest practical window size will minimize the data size without significant loss of accuracy. For practical application, calibration is necessary to achieve consistent roughness measurements between various different smartphones.
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    Road Roughness Evaluation Using In-Pavement Strain Sensors
    (2015) Zhang, Zhiming; Deng, Fodan; Huang, Ying; Bridgelall, Raj; Upper Great Plains Transportation Institute
    The international roughness index (IRI) is a characterization of road roughness or ride quality that transportation agencies most often report. The prevalent method of acquiring IRI data requires instrumented vehicles and technicians with specialized training to interpret the results. The extensive labor and high cost requirements associated with the existing approaches limit data collection to at most once per year for portions of the national highway system. Agencies characterize roughness only for some secondary roads but much less frequently, such as once every five years, resulting in outdated roughness information. This research developed a real-time roughness evaluation approach that links the output of durable in-pavement strain sensors to prevailing indices that summarize road roughness. Field experiments validated the high consistency of the approach by showing that it is within 3.3% of relative IRI estimates. After their installation and calibration during road construction, the ruggedized strain sensors will report road roughness continuously. Thus, the solution will provide agencies a real-time roughness monitoring solution over the remaining service life of road assets.
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    Vehicle Axle Detection from Under-Sampled Signal through Compressed-Sensing-Based Signal Recovery
    (2022) Zhang, Zhiming; Huang, Ying; Bridgelall, Raj; Upper Great Plains Transportation Institute
    In traffic data collection, sampling design should satisfy the requirements of identifying prominent pulses corresponding to vehicle axle passage. Insufficient measurement leads to signal distortion and attenuation, reducing the quality of signal pulses. This study exploits the value of under-sampled data by applying compressed sensing (CS) methods to recover signal components that are critical for vehicle axle detection. Two CS methods are investigated in this study to recover the strain signal pulses from inside-pavement instrumented sensors at high-speed traversals. The CS methods successfully recovered the signal pulses from all axles of the truck used for testing. A comparison of the measured axle distances with the reference measurements validated the effectiveness of signal recovery methods. Therefore, the CS methods have the potential of reducing the cost, energy consumption, and data storage space, and improving the data transmission efficiency in practical implementations by enabling sampling devices designed for static measurements to achieve dynamic measurements.
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    Weigh-In-Motion System in Flexible Pavements Using Fiber Bragg Grating Sensors Part A: Concept
    (2019) Al-Tarawneh, Mu'ath; Huang, Ying; Lu, Pan; Bridgelall, Raj; Upper Great Plains Transportation Institute
    Weight data of vehicles play an important role in traffic planning, weight enforcement, and pavement condition assessment. In this paper, a weigh-in-motion (WIM) system that functions at both low-speeds and high-speeds in flexible pavements is developed based on in-pavement, three-dimensional glass-fiber-reinforced, polymer-packaged fiber Bragg grating sensors (3D GFRP-FBG). Vehicles passing over the pavement produce strains that the system monitors by measuring the center wavelength changes of the embedded 3D GFRP-FBG sensors. The FBG sensor can estimate the weight of vehicles because of the direct relationship between the loading on the pavement and the strain inside the pavement. A sensitivity study shows that the developed sensor is very sensitive to sensor installation depth, pavement property, and load location. Testing in the field validated that the longitudinal component of the sensor if not corrected by location has a measurement accuracy of 86.3% and 89.5% at 5 mph and 45 mph vehicle speed, respectively. However, the system also has the capability to estimate the location of the loading position, which can enhance the system accuracy to more than 94.5%.