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Item Forecasting the Effects of Autonomous Vehicles on Land Use(2020) Bridgelall, Raj; Stubbing, Edward; Upper Great Plains Transportation InstituteThe 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.Item Characterizing Pavement Roughness at Non-Uniform Speeds Using Connected Vehicles(2017) Bridgelall, Raj; Hough, Jill; Tolliver, Denver D.; Upper Great Plains Transportation InstituteMethods 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.Item Hyperspectral Range Imaging for Transportation Systems Evaluation(2016) Bridgelall, Raj; Rafert, J. Bruce; Tolliver, Denver D.; Atwood, Don; Upper Great Plains Transportation InstituteTransportation 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.Item Inertial Sensor Sample Rate Selection for Ride Quality Measures(2014) Bridgelall, Raj; Upper Great Plains Transportation InstituteThe 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.Item Pavement Performance Evaluations Using Connected Vehicles(2016) Bridgelall, Raj; Upper Great Plains Transportation InstituteThe 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.Item Wavelength Sensitivity of a Connected Vehicle Method of Ride Quality Characterizations(2017) Bridgelall, Raj; Rahman, Md Tahmidur; Tolliver, Denver D.; Daleiden, Jerome F.; Upper Great Plains Transportation InstituteResearchers previously demonstrated that a roughness index called the road impact factor (RIF) is directly proportional to the international roughness index (IRI) when measured under identical conditions. A RIF-transform converts inertial signals from connected vehicle accelerometers and speed sensors to produce RIF-indices in realtime. This research examines the relative sensitivities of the RIF and the IRI to variations in dominant profile wavelengths. The findings are that both indices characterize roughness from spatial wavelengths up to 2 meters with equal sensitivity. However, the RIF transform maintains its sensitivity when characterizing roughness from wavelengths beyond that. The case studies used a certified inertial profiler to collect both RIF and IRI data simultaneously from five different pavement surface types. The RIF/IRI proportionality factors distributed normally among the profiles tested. This result affirms that the RIF and IRI generally agrees. However, differences in the dominant profile wavelength among pavements will produce some spread in the degree of roughness that the indices express.Item Mining Connected Vehicle Data for Beneficial Patterns in Dubai Taxi Operations(2018) Bridgelall, Raj; Lu, Pan; Tolliver, Denver D.; Xu, Tie; Upper Great Plains Transportation InstituteOn-demand shared mobility services such as Uber and micro-transit are steadily penetrating the worldwide market for traditional dispatched taxi services. Hence, taxi companies are seeking ways to compete. This study mined large-scale mobility data from connected taxis to discover beneficial patterns that may inform strategies to improve dispatch taxi business. It is not practical to manually clean and filter large-scale mobility data that contains GPS information. Therefore, this research contributes and demonstrates an automated method of data cleaning and filtering that is suitable for such types of datasets. The cleaning method defines three filter variables and applies a layered statistical filtering technique to eliminate outlier records that do not contribute to distributions that match expected theoretical distributions of the variables. Chi-squared statistical tests evaluate the quality of the cleaned data by comparing the distribution of the three variables with their expected distributions. The overall cleaning method removed approximately 5% of the data, which consisted of errors that were obvious and others that were poor quality outliers. Subsequently, mining the cleaned data revealed that trip production in Dubai peaks for the case when only the same two drivers operate the same taxi. This finding would not have been possible without access to proprietary data that contains unique identifiers for both drivers and taxis. Datasets that identify individual drivers are not publicly available.Item Technology Developments and Impacts of Connected and Autonomous Vehicles: An Overview(2022) Ahmed, Hafiz Usman; Huang, Ying; Lu, Pan; Bridgelall, Raj; Upper Great Plains Transportation InstituteThe scientific advancements in the vehicle and infrastructure automation industry are progressively improving nowadays to provide benefits for the end-users in terms of traffic congestion reduction, safety enhancements, stress-free travels, fuel cost savings, and smart parking, etc. The advances in connected, autonomous, and connected autonomous vehicles (CV, AV, and CAV) depend on the continuous technology developments in the advanced driving assistance systems (ADAS). A clear view of the technology developments related to the AVs will give the users insights on the evolution of the technology and predict future research needs. In this paper, firstly, a review is performed on the available ADAS technologies, their functions, and the expected benefits in the context of CVs, AVs, and CAVs such as the sensors deployed on the partial or fully automated vehicles (Radar, LiDAR, etc.), the communication systems for vehicle-to-vehicle and vehicle-to-infrastructure networking, and the adaptive and cooperative adaptive cruise control technology (ACC/CACC). Secondly, for any technologies to be applied in practical AVs related applications, this study also includes a detailed review in the state/federal guidance, legislation, and regulations toward AVs related applications. Last but not least, the impacts of CVs, AVs, and CAVs on traffic are also reviewed to evaluate the potential benefits as the AV related technologies penetrating in the market. Based on the extensive reviews in this paper, the future related research gaps in technology development and impact analysis are also discussed.Item Precision Bounds of Pavement Distress Localization with Connected Vehicle Sensors(2014) Bridgelall, Raj; Upper Great Plains Transportation InstituteContinuous, 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.