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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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    A Fuzzy Delphi Analytic Hierarchy Model to Rank Factors Influencing Public Transit Mode Choice: A Case Study
    (2020) Ebrahimi, Sajad; Bridgelall, Raj; Upper Great Plains Transportation Institute
    This study applied a decision-based model with uncertainty to identify factors in mode choice and to rank their influence in attracting riders to available public transit modes in the city of Tehran. The model integrates a fuzzy Delphi method and a fuzzy analytic hierarchy process with fuzzy set theory to process opinion uncertainties. The surveys found that from highest to lowest in influence, the service attribute rankings were safety, reliability, frequency, comfort, travel cost, information provision, and accessibility. Based on these attributes, subway ranked highest in passenger attraction potential, followed by ride-hailing, bus rapid transit, vans and taxis, then public bus services. These findings support the hypothesis that it is worthwhile for big cities to ramp investments in public transit improvements even as ride hailing services proliferate with the potential to attract users away from more throughput-efficient and lower-cost services.
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    Characterizing Ride Quality With a Composite Roughness Index
    (2022) Bridgelall, Raj; Upper Great Plains Transportation Institute
    There are many important applications that require ride quality characterization. However, the only international standard that specifies a roughness index is not suitable for applications beyond assessing the ride quality of paved roads. Other potential applications include automated ride quality characterization of gravel roads, bike or wheelchair paths, railways, rivers, airways, hyperloops, and elevator channels. This work proposes a composite index that characterizes roughness from multidimensional movements along any path. Statistical tests demonstrate two important properties—that the index is consistent based on an ever-decreasing margin-of-error of the mean, and distinguishable among different paths. A low-cost sensor package of accelerometers, gyroscopes, and a speedometer produced the data for spatio-temporal transformation. The experiments conducted on buses revealed that both the consistency and distinguishability of the index improves with the number of measurements. The approach is best suited for applications that can use in-situ sensors or crowdsensing to automate ride quality characterization.
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    Forecasting the Effects of Autonomous Vehicles on Land Use
    (2020) Bridgelall, Raj; Stubbing, Edward; Upper Great Plains Transportation Institute
    The widespread availability of connected and autonomous vehicles (CAVs) will likely affect social change in terms of how people travel. Traditional methods of travel demand and land use modeling require vast amounts of data that could be expensive to obtain. Such models use complex software that requires trained professionals to configure and hours to run a single scenario. Alternative closed form models that can quickly assess trends in potential CAV impact on the regional demand for shopping, entertainment, or dining land use does not exist. This research developed a closed-form model that considers the potential mode shift towards CAVs, possible changes in the propensity to travel, shopping trip avoidance from e-commerce, and greater accessibility for non-drivers. Model parameter estimation based on statistics from the greater Toronto area found that population growth from 2017 to 2050 alone could increase the demand for shopping, entertainment, or dining land use by nearly 60%. However, CAVs could double or triple that demand—implicating dynamic planning and environmental considerations.
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    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.
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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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    Unlocking Drone Potential in the Pharma Supply Chain: A Hybrid Machine Learning and GIS Approach
    (2023) Bridgelall, Raj; Upper Great Plains Transportation Institute
    In major metropolitan areas, the growing levels of congestion pose a significant risk of supply chain disruptions by hindering surface transportation of commodities. To address this challenge, cargo drones are emerging as a potential mode of transport that could improve the reliability of the pharmaceutical supply chain and enhance healthcare. This study proposes a novel hybrid workflow that combines machine learning and a geographic information system to identify the fewest locations where providers can initiate cargo drone services to yield the greatest initial benefits. The results show that by starting a service in only nine metropolitan areas across four regions of the contiguous United States, drones with a robust 400-mile range can initially move more than 28% of the weight of all pharmaceuticals. The medical community, supply chain managers, and policymakers worldwide can use this workflow to make data-driven decisions about where to access the largest opportunities for pharmaceutical transport by drones. The proposed approach can inform policies and standards such as Advanced Air Mobility to help address supply chain disruptions, reduce transportation costs, and improve healthcare outcomes.
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    Literature Review of Socioeconomic and Environmental Impacts of High-Speed Rail in the World
    (2021) Momenitabar, Mohsen; Bridgelall, Raj; Dehdari Ebrahimi, Zhila; Arani, Mohammad; Upper Great Plains Transportation Institute
    Countries considering high-speed rail (HSR) developments face enormous challenges because of their high deployment cost, environmental obstacles, political opposition, and their potentially adverse effects on society. Nevertheless, HSR services are importantly sustainable that can have positive and transformative effects on the economic growth of a nation. This paper systematically reviews and classifies impact areas of HSR deployments around the world as well as the analytical methods used to evaluate those impacts. We have utilized the scholarly scientific database to find articles in HSR systems. By defining some rules, we select 116 articles between 1997 and March 2020. The approach revealed interesting patterns and trends in space, time, and sentiment of the analyzed impacts on society, the economy, and the environment. The findings can inform decision-making about HSR developments and deployments, and the gaps identified in the literature can propose new research opportunities for future studies.
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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.