Transportation, Logistics, and Finance

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Enabling Mobile Commerce through Pervasive Communications with Ubiquitous RF Tags
(2003) Bridgelall, Raj; Upper Great Plains Transportation Institute
For many years we’ve heard of the existence of a wonderful new technology called radio frequency identification (RFID) that allows supermarket items to be checked out without human intervention. Advertisements claim that this technology will be able to locate our keys and spectacles when we lose them around the house - all for pennies. Although technologists amongst us widely recognize this as very early marketing hype, we also admit to having recently witnessed strong evidence that underlying RFID tag performance and cost are fast approaching these initially very optimistic expectations. The future success of mobile commerce or m-commerce will depend on a pervasive communications infrastructure that provides both seamless roaming and automatic object identification. In this paper, we identify key factors that will enable future pervasive deployment of RFID tag and communications technology, thereby leading to the acceleration of applications for m-commerce. For each of these key factors, we provide a summary of the existing impediments and propose potential solutions.
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.
Introducing a Micro-Wireless Architecture for Business Activity Sensing
(2008) Bridgelall, Raj; Upper Great Plains Transportation Institute
RFID performance deficiencies discovered in recent high profile applications have highlighted the danger of selecting only passive tags for an application because of their lowest cost relative to other types of RFID tags. Consequentially, battery-based RFID technologies are being considered to fill those performance gaps. A mix of both passive and battery-based RFID technologies can provide a more cost effective and robust solution than a homogeneous RFID deployment. However, it is easy to choose the wrong battery-based RFID technology given the confusing array of choices currently available. This paper explores the performance deficiencies of both passive and battery-based RFID technologies. A new micro-wireless technology that resolves these performance deficiencies is then introduced. Finally an application example is presented that demonstrates how the new technology can also seamlessly roam between passive and battery-based RFID infrastructures at the lowest possible cost to bridge their respective performance gaps.
Vibration Energy Harvesting for Disaster Asset Monitoring Using Active RFID Tags
(2010) Bridgelall, Raj; Hande, Abhiman; Zoghi, Ben; Upper Great Plains Transportation Institute
This paper highlights the importance of energy harvesting in high-value asset monitoring applications involving use of active RFID tags. The paper begins by highlighting advantages of active tags including improved range and read rate in electromagnetically unfriendly environments. Although a battery can substantially improve performance, it limits maintenance-free operational life. Therefore, harvesting energy from sources such as vibration is shown to address this shortcoming but these sources must be adequate, available throughout the life of the application, and highly efficient. Piezoelectric vibration energy harvesting design procedures and components for such systems are identified. This includes three key components namely, the energy harvesting transducer, power management circuit, and energy storage device. Each component of the energy harvesting system is described and important design criteria are highlighted. Finally, the paper concludes by analyzing vibration data from high value assets used during disaster relief, and describing preliminary results of an energy harvesting prototype with details on system form factors, efficiency, and life.
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.
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.
A Connected Vehicle Approach for Pavement Roughness Evaluation
(2014) Bridgelall, Raj; Upper Great Plains Transportation Institute
Connected vehicles present an opportunity to monitor pavement condition continuously by analyzing data from vehicle-integrated position sensors and accelerometers. The current practice of characterizing and reporting ride-quality is to compute the international roughness index (IRI) from elevation profile or bumpiness measurements. However, the IRI is defined only for a reference speed of 80 kilometers per hour. Furthermore, the relatively high cost for calibrated instruments and specialized expertise needed to produce the IRI limit its potential for widespread use in a connected vehicle environment. This research introduces the road impact factor (RIF) which is derived from vehicle integrated accelerometer data. The analysis demonstrates that RIF and IRI are directly proportional. Simultaneous data collection with a laser-based inertial profiler validates this relationship. A linear combination of the RIF from different speed bands produces a time-wavelength-intensity-transform (TWIT) that, unlike the IRI, is wavelength-unbiased. Consequently, the TWIT enables low-cost, network-wide and repeatable performance measures at any speed. It can extend models that currently use IRI data by calibrating them with a constant of proportionality.
A Participatory Sensing Approach to Characterize Ride Quality
(2014) Bridgelall, Raj; Upper Great Plains Transportation Institute
Rough roads increase vehicle operation and road maintenance costs. Consequently, transportation agencies spend a significant portion of their budgets on ride-quality characterization to forecast maintenance needs. The ubiquity of smartphones and social media, and the emergence of a connected vehicle environment present lucrative opportunities for cost-reduction and continuous, network-wide, ride-quality characterization. However, there is a lack of models to transform inertial and position information from voluminous data flows into indices that transportation agencies currently use. This work expands on theories of the Road Impact Factor introduced in previous research. The index characterizes road roughness by aggregating connected vehicle data and reporting roughness in direct proportion to the International Roughness Index. Their theoretical relationships are developed, and a case study is presented to compare the relative data quality from an inertial profiler and a regular passenger vehicle. Results demonstrate that the approach is a viable alternative to existing models that require substantially more resources and provide less network coverage. One significant benefit of the participatory sensing approach is that transportation agencies can monitor all network facilities continuously to locate distress symptoms, such as frost heaves, that appear and disappear between ride assessment cycles. Another benefit of the approach is continuous monitoring of all high-risk intersections such as rail grade crossings to better understand the relationship between ride-quality and traffic safety.
Precision Bounds of Pavement Deterioration Forecasts from Connected Vehicles
(2014) Bridgelall, Raj; Upper Great Plains Transportation Institute
Transportation agencies rely on models to predict when pavements will deteriorate to a condition or ride-index threshold that triggers maintenance actions. The accuracy and precision of such forecasts are directly proportional to the frequency of monitoring. Ride indices derived from connected vehicle sensor data will enable transformational gains in both the accuracy and precision of deterioration forecasts because of very high data volume and update rates. This analysis develops theoretical precision bounds for a ride index called the road impact factor and demonstrates, via a case study, its relationship with vehicle suspension parameter variances.
Campus Parking Supply Impacts on Transportation Mode-Choice
(2014) Bridgelall, Raj; Upper Great Plains Transportation Institute
Parking demand is a significant land-use problem in campus planning. The parking policies of universities and large corporations with facilities located in small urban areas shape the character of their campuses. These facilities will benefit from a simplified methodology to study the effects of parking availability on transportation mode mix and impacts on recruitment and staffing policies. This study introduces an analytical framework Using simple models to provide campus planners with insights about how parking supply and demand affects campus transportation mode choice. The methodology relies only on aggregate mode choice data for the special generator zone and the average aggregate volume/capacity ratio projections for all external routes that access the zone. This reduced data requirement significantly lowers the analysis cost and time and obviates the need for specialized modelling software and spatial network analysis tools. Results illustrate that the framework is effective for analysing mode choice changes under different scenarios of parking supply and population growth.
Precision Bounds of Pavement Distress Localization with Connected Vehicle Sensors
(2014) Bridgelall, Raj; Upper Great Plains Transportation Institute
Continuous, network-wide monitoring of pavement performance will significantly reduce risks and provide an adequate volume of timely data to enable accurate maintenance forecasting. Unfortunately, transportation agencies can afford to monitor less than 4% of the nation’s roads. Even so, agencies monitor their ride quality at most once annually because current methods are expensive and laborious. Distributed mobile sensing with connected vehicles and smartphones could provide a viable solution at much lower costs. However, such approaches lack models that improve with continuous, high-volume data flows. This research characterizes the precision bounds of the Road Impact Factor transform that aggregates voluminous data feeds from geoposition and inertial sensors in vehicles to locate potential road distress symptoms. Six case studies of known bump traversals reveal that vehicle suspension transient motion and sensor latencies are the dominant factors in position estimate errors and uncertainty levels. However, for a typical vehicle mix, the precision improves substantially as the number of traversals approaches 50.
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.
Context Sensitive Solution: A Case Study of Northwest Highway White Rock Lake, Dallas in Texas
(2015) Bridgelall, Raj; Lee, EunSu; Upper Great Plains Transportation Institute
Loop 12 is the first ring around the city of Dallas. The project is a three-quarter mile section of Loop 12 on Northwest Highway. The project section of Northwest Highway is a set of six bridges that cross a 100-year floodplain. The environmental challenges, the diversity of stakeholders and their needs, and heightened sensitivities from special interest groups posed significant challenges for this project. Texas Department of Transportation (TxDOT) initially identified the traditional stakeholder groups to be representatives of area residences, school, small businesses, highway users, and transportation providers. However, the unique setting for this project also created a number non-traditional stakeholder groups. A major construction that would last a few years would substantially disrupt their normal activities. These groups were particularly sensitive to changes in the environment as TxDOT leaned after contracting HNTB Corporation to research the community and its history. From the project, we learned that forming multidisciplinary and hierarchical teams is one of the key factors for a successful project. Early and regular engagement of the public helps the environmental assessment and project progress. Visual simulation is one of the effective tools to communicate with the public. Inter-agency coordination is critical. Traffic management strategies must adapt with context sensitive solutions of transportation projects.
Hyperspectral Imaging Utility for Transportation Systems
(2015) Bridgelall, Raj; Rafert, J. Bruce; Tolliver, Denver D.; Upper Great Plains Transportation Institute
The global transportation system is massive, open, and dynamic. Existing performance and condition assessments of the complex interacting networks of roadways, bridges, railroads, pipelines, waterways, airways, and intermodal ports are expensive. Hyperspectral imaging is an emerging remote sensing technique for the non-destructive evaluation of multimodal transportation infrastructure. Unlike panchromatic, color, and infrared imaging, each layer of a hyperspectral image pixel records reflectance intensity from one of dozens or hundreds of relatively narrow wavelength bands that span a broad range of the electromagnetic spectrum. Hence, every pixel of a hyperspectral scene provides a unique spectral signature that offers new opportunities for informed decision-making in transportation systems development, operations, and maintenance. Spaceborne systems capture images of vast areas in a short period but provide lower spatial resolution than airborne systems. Practitioners use manned aircraft to achieve higher spatial and spectral resolution, but at the price of custom missions and narrow focus. The rapid size and cost reduction of unmanned aircraft systems promise a third alternative that offers hybrid benefits at affordable prices by conducting multiple parallel missions. This research formulates a theoretical framework for a pushbroom type of hyperspectral imaging system on each type of data acquisition platform. The study then applies the framework to assess the relative potential utility of hyperspectral imaging for previously proposed remote sensing applications in transportation. The authors also introduce and suggest new potential applications of hyperspectral imaging in transportation asset management, network performance evaluation, and risk assessments to enable effective and objective decision- and policy-making.
Hyperspectral Applications in the Global Transportation Infrastructure
(2015) Bridgelall, Raj; Rafert, J. Bruce; Tolliver, Denver D.; Upper Great Plains Transportation Institute
Hyperspectral remote sensing is an emerging field with potential applications in the observation, management, and maintenance of the global transportation infrastructure. This study introduces a general analytical framework to link transportation systems analysis and hyperspectral analysis. The authors introduce a range of applications that would benefit from the capabilities of hyperspectral remote sensing. They selected three critical but unrelated applications and identified both the spatial and spectral information of their key operational characteristics to demonstrate the hyperspectral utility. The specific scenario studies exemplifies the general approach of utilizing the outputs of hyperspectral analysis to improve models that practitioners currently use to analyze a variety of transportation problems including roadway congestion forecasting, railway condition monitoring, and pipeline risk management.
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.
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.
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.
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.
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.

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