Transportation, Logistics, and Finance

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Browsing Transportation, Logistics, and Finance by Author "Chia, Leonard"

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    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.
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    Railroad Track Condition Monitoring Using Inertial Sensors and Digital Signal Processing: A Review
    (2018) Chia, Leonard; Bhardwaj, Bhavana; Lu, Pan; Bridgelall, Raj; Upper Great Plains Transportation Institute
    Inertial sensors such as accelerometers and gyroscopes have been widely used since the early 1990s to monitor the condition of transportation assets. Recent improvements in their performance, a reduction in cost, and sensor miniaturization has resulted in a growing interest expanding their use. This research is an extensive and systematic review of their application considerations, challenges, and opportunities for improvements in railroad track condition monitoring. Research questions were developed to guide the selection of relevant articles from databases. The authors report key findings in the areas of sensor specification, sensor location, and sensor signal processing.
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    Signal Filter Cut-off Frequency Determination to Enhance the Accuracy of Rail Track Irregularity Detection and Localization
    (2019) Bhardwaj, Bhavana; Bridgelall, Raj; Chia, Leonard; Lu, Pan; Dhingra, Neeraj; Upper Great Plains Transportation Institute
    A continuous condition monitoring system to detect and localize railroad track irregularities is achievable with inertial sensors onboard revenue service trains. However, the inaccurate geospatial position estimates of GPS receivers and the non-uniform sampling of inertial sensors adds noise and reduces signal strength. Consequently, the signal-to-noise ratio decreases, which leads to higher rates of false positives and false negatives. Appropriate signal filtering, alignment, and combination from multiple traversals can enhance the signal-to-noise ratio. However, it is not straightforward to determine the best cut-off frequency for the filter. This paper introduces a method that is suitable for any signal filtering approach. The frequency window of the resultant energy and variance of ensemble averaged FFTs informs the best cut-off frequency. The results affirm that a lowpass finite impulse response filter with the selected cutoff frequency progressively increases the signal-to-noise ratio with increasing filter order, thus demonstrating the effectiveness and practicality of the method.