Browsing by Author "Bridgelall, Raj"

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Accuracy Enhancement of Anomaly Localization with Participatory Sensing Vehicles
(2020) Bridgelall, Raj; Tolliver, Denver D.; Upper Great Plains Transportation Institute
Transportation agencies cannot afford to scale existing methods of roadway and railway condition monitoring to more frequently detect, localize, and fix anomalies throughout networks. Consequently, anomalies such as potholes and cracks develop between maintenance cycles and cause severe vehicle damage and safety issues. The need for a lower-cost and more-scalable solution spurred the idea of using sensors on board vehicles for a continuous and network-wide monitoring approach. However, the timing of the full adoption of connected vehicles is uncertain. Therefore, researchers used smartphones to evaluate a variety of methods to implement the application using regular vehicles. However, the poor accuracy of standard positioning services with low-cost geospatial positioning system (GPS) receivers presents a significant challenge. The experiments conducted in this research found that the error spread can exceed 32 m, and the mean localization error can exceed 27 m at highway speeds. Such large errors can make the application impractical for widespread use. This work used statistical techniques to inform a model that can provide more accurate localization. The proposed method can achieve sub-meter accuracy from participatory vehicle sensors by knowing only the mean GPS update rate, the mean traversal speed, and the mean latency of tagging accelerometer samples with GPS coordinates.
Accuracy Enhancement of Roadway Anomaly Localization Using Connected Vehicles
(2016) Bridgelall, Raj; Tolliver, Denver D.; Upper Great Plains Transportation Institute
The timely identification and localization of roadway anomalies that pose hazards to the traveling public is currently a critical but very expensive task. Hence, transportation agencies are evaluating emerging alternatives that use connected vehicles to lower the cost dramatically and to increase simultaneously both the monitoring frequency and the network coverage. Connected vehicle methods use conventional GPS receivers to tag the inertial data stream with geospatial position estimates. In addition to the anticipated GPS trilateration errors, numerous other factors reduce the accuracy of anomaly localization. However, practitioners currently lack information about their characteristics and significance. This study developed error models to characterize the factors in position biases so that practitioners can estimate and remove them. The field studies revealed the typical and relative contributions of each factor, and validated the models by demonstrating agreement of their statistics with the anticipated norms. The results revealed a surprising potential for tagging errors from embedded systems latencies to exceed the typical GPS errors and become dominant at highway speeds.
An Application of Natural Language Processing to Classify What Terrorists Say They Want
(2022) Bridgelall, Raj; Upper Great Plains Transportation Institute
Knowing what perpetrators want can inform strategies to achieve safe, secure, and sustainable societies. To help advance the body of knowledge in counterterrorism, this research applied natural language processing and machine learning techniques to a comprehensive database of terrorism events. A specially designed empirical topic modeling technique provided a machine-aided human decision process to glean six categories of perpetrator aims from the motive text narrative. Subsequently, six different machine learning models validated the aim categories based on the accuracy of their association with a different narrative field, the event summary. The ROC-AUC scores of the classification ranged from 86% to 93%. The Extreme Gradient Boosting model provided the best predictive performance. The intelligence community can use the identified aim categories to help understand the incentive structure of terrorist groups and customize strategies for dealing with them.
Applying Unsupervised Machine Learning to Counterterrorism
(2022) Bridgelall, Raj; Upper Great Plains Transportation Institute
To advance the agenda in counterterrorism, this work demonstrates how analysts can combine unsupervised machine learning, exploratory data analysis, and statistical tests to discover features associated with different terrorist motives. A new empirical text mining method created a “motive” field in the Global Terrorism Database to enable associative relationship mining among features that characterize terrorist events. The methodology incorporated K-means co-clustering, three methods of non-linear projection, and two spatial association tests to reveal statistically significant relationships between terrorist motives, tactics, and targets. Planners and investigators can replicate the approach to distill knowledge from big datasets to help advance the state of the art in counterterrorism.
Attack Risk Modelling for the San Diego Maritime Facilities
(2020) Patterson, Douglas A.; Bridgelall, Raj; Upper Great Plains Transportation Institute
California is the largest economy among states in the US. More than 40% of the nation's containerized cargo flows through the marine ports of California. Cruise ships also call on four of California's largest ports, with the Port of San Diego growing the fastest. This study assessed the attack risk for the Port of San Diego by applying a model from the risk assessment framework recommended by the Department of Homeland Security. Quantification of the model's threat and consequence factors was based on economic data derived from various databases. The findings show that the risk of attack is more pronounced for cruise ship operations than for marine cargo operations. The risk is directly proportional to the level of vulnerability to expected perpetrator tactics and weapons. Based on expert knowledge of the port characteristics, the vulnerability assessment points to a low probability that anticipated attack methods could succeed. However, the behavior of terrorists can be unpredictable as they continuously adapt to exploit vulnerability gaps that may be unforeseen. Therefore, it is wise to develop policies that encourage a security culture to avoid complacency and to conduct regular risk assessments.
Budgeting the Adoption of Sensors on Connected Trains
(2021) Bridgelall, Raj; Tolliver, Denver D.; Upper Great Plains Transportation Institute
Railroads can save millions of dollars by deploying multi-sensor track scanners on connected trains to detect track and roadbed problems that could cause accidents. However, uncertainties about performance and return-on-investment impeded the development and deployment of such sensor systems. This research develops a budget model that both manufacturers and railroads can use to decide on a suitable tradeoff between price affordability and achievable performance. A case study of five Class 1 railroads demonstrates that a payback within two years is achievable at $4,000 per device and an annual maintenance cost of one-quarter the system deployment cost.
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.
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.
Characterizing Pavement Roughness at Non-Uniform Speeds Using Connected Vehicles
(2017) Bridgelall, Raj; Hough, Jill; Tolliver, Denver D.; Upper Great Plains Transportation Institute
Methods of pavement roughness characterizations using connected vehicles are poised to scale beyond the frequency, span, and affordability of existing methods that require specially instrumented vehicles and skilled technicians. However, speed variability and differences in suspension behavior require segmentation of the connected vehicle data to achieve some level of desired precision and accuracy with relatively few measurements. This study evaluates the reliability of a Road Impact Factor (RIF) transform under stop-and-go conditions. A RIF-transform converts inertial signals from on-board accelerometers and speed sensors to roughness indices (RIF-indices), in real-time. The case studies collected data from 18 different buses during their normal operation in a small urban city. Within 30 measurements, the RIF-indices distributed normally with an average margin-of-error below 6%. This result indicates that a large number of measurements will provide a reliable estimate of the average roughness experienced. Statistical t-tests distinguished the relatively small differences in average roughness levels among the roadway segments evaluated. In conclusion, when averaging roughness measurements from the same type of vehicle moving at non-uniform speeds, the RIF-transform will provide everincreasing precision and accuracy as the traversal volume increases.
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.
Closed Form Models to Assess Railroad Technology Investments
(2020) Bridgelall, Raj; Tolliver, Denver D.; Upper Great Plains Transportation Institute
Class I railroads in North America collectively invested $11.2 billion to comply with a federal mandate to deploy positive train control. This amount dwarfs the potential savings from accidents the technology could prevent. Therefore, railroads must seek additional benefits. This research contributes simple closed-form models to inform strategies that can leverage the technology deployment by estimating the annual additional net benefits, internal rate of return, and benefit-cost ratio needed for a desired payback period.
A Cognitive Framework to Plan for the Future of Transportation
(2020) Bridgelall, Raj; Tolliver, Denver D.; Upper Great Plains Transportation Institute
Automated, connected, electrified, and shared mobility will be cornerstones of the transportation future. Research to quantify the potential benefits and drawbacks of practice, and to identify barriers to adoption is the first step in any strategic plan for their adoption. However, uncertainties, complexity, interdependence, and the multidisciplinary nature of emerging transportation technologies make it difficult to organize and identify focused research. The contribution of this work is a cognitive framework to help planners and policy-makers organize broad topics, reveal challenges, discover ideas for solutions, quantify potential impacts, and identify implications to guide preparation strategies. The authors provide example cognitive frameworks for connected, automated, and electrified vehicles.
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.
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.
Data-Driven Deployment of Cargo Drones: A U.S. Case Study Identifying Key Markets and Routes
(2023) Bridgelall, Raj; Upper Great Plains Transportation Institute
Electric and autonomous aircraft (EAA) are set to disrupt current cargo-shipping models. To maximize the benefits of this technology, investors and logistics managers need information on target commodities, service location establishment, and the distribution of origin–destination pairs within EAA’s range limitations. This research introduces a three-phase data-mining and geographic information system (GIS) algorithm to support data-driven decision-making under uncertainty. Ana- lysts can modify and expand this workflow to scrutinize origin–destination commodity flow datasets representing various locations. The algorithm identifies four commodity categories contributing to more than one-third of the value transported by aircraft across the contiguous United States, yet only 5% of the weight. The workflow highlights 8 out of 129 regional locations that moved more than 20% of the weight of those four commodity categories. A distance band of 400 miles among these eight locations accounts for more than 80% of the transported weight. This study addresses a literature gap, identifying opportunities for supply chain redesign using EAA. The presented methodology can guide planners and investors in identifying prime target markets for emerging EAA technologies using regional datasets.
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.
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.
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.
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.