Transportation, Logistics, and Finance

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Browsing Transportation, Logistics, and Finance by Author "Dhingra, Neeraj"

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    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.
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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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    Ranking Risk Factors in Financial Losses From Railroad Incidents: A Machine Learning Approach
    (2023) Dhingra, Neeraj; Bridgelall, Raj; Lu, Pan; Szmerekovsky, Joseph; Bhardwaj, Bhavana; Upper Great Plains Transportation Institute
    The reported financial losses from railroad accidents since 2009 have been more than US$4.11 billion dollars. This considerable loss is a major concern for the industry, society, and the government. Therefore, identifying and ranking the factors that contribute to financial losses from railroad accidents would inform strategies to minimize them. To achieve that goal, this paper evaluates and compares the results of applying different non-parametric statistical and regression methods to 15 years of railroad Class I freight train accident data. The models compared are random forest, k-nearest neighbors, support vector machines, stochastic gradient boosting, extreme gradient boosting, and stepwise linear regression. The results indicate that these methods are all suitable for analyzing non-linear and heterogeneous railroad incident data. However, the extreme gradient boosting method provided the best performance. Therefore, the analysis used that model to identify and rank factors that contribute to financial losses, based on the gain percentage of the prediction accuracy. The number of derailed freight cars and the absence of territory signalization dominated as contributing factors in more than 57% and 20% of the accidents, respectively. Partial-dependence plots further explore the complex non-linear dependencies of each factor to better visualize and interpret the results.
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    Signal Feature Extraction and Combination to Enhance the Detection and Localization of Railroad Track Irregularities
    (2020) Bhardwaj, Bhavana; Bridgelall, Raj; Lu, Pan; Dhingra, Neeraj; Upper Great Plains Transportation Institute
    Tracks are critical and expensive railroad asset, requiring frequent maintenance. The stress from heavy car axle loads increases the risk of deviations from uniform track geometry. Irregularities in track geometry, such as track warping, can cause an excessive harmonic rocking condition that can lead to derailments, traffic delays, and associated financial losses. This paper presents an approach to enhance the location identification accuracy of track geometry irregularities by combining measurements from sensors aboard Hi-Rail vehicles. However, speed variations, position recording errors, low GPS update rates, and the non-uniform sampling rates of inertial sensors pose significant challenges for signal processing, feature extraction, and signal combination. This study introduces a method of extracting features from the fused data of inertial sensors and GPS receivers with multiple traversals to locate and characterize irregularities of track geometry. The proposed method provides robust detection and enhanced accuracy in the localization of irregularities within spatial windows along the track segment. Tradeoff analysis found that the optimal spatial window size is 5-meter.
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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.