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Item Precision Enhancement of Pavement Roughness Localization with Connected Vehicles(2016) Bridgelall, Raj; Huang, Y.; Zhang, Z.; Deng, F.; Upper Great Plains Transportation InstituteTransportation 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.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 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 A Connected Vehicle Approach for Pavement Roughness Evaluation(2014) Bridgelall, Raj; Upper Great Plains Transportation InstituteConnected 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.Item A Participatory Sensing Approach to Characterize Ride Quality(2014) Bridgelall, Raj; Upper Great Plains Transportation InstituteRough 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.Item Precision Bounds of Pavement Deterioration Forecasts from Connected Vehicles(2014) Bridgelall, Raj; Upper Great Plains Transportation InstituteTransportation 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.Item Error Sensitivity of the Connected Vehicle Approach to Pavement Performance Evaluations(2016) Bridgelall, Raj; Rahman, Md Tahmidur; Tolliver, Denver D.; Daleiden, Jerome F.; Upper Great Plains Transportation InstituteThe international roughness index is the prevalent indicator used to assess and forecast road maintenance needs. The fixed parameters of its simulation model provide the advantage of requiring relatively few traversals to produce a consistent index. However, the static parameters also cause the model to under-represent roughness that riders experience from profile wavelengths outside of the model’s response range. A connected vehicle method that uses a similar but different index to characterize roughness can do so by accounting for all vibration wavelengths that the actual vehicles experience. This study characterizes and compares the precision of each method. The field studies indicate that within 7 traversals, the connected vehicle approach could achieve the same level of precision as the procedure used to produce the international roughness index. For a given vehicle and segment lengths longer than 50 meters, the margin-of-error diminished below 1.5% after 50 traversals, and continued to improve further as the traversal volume grew. Practitioners developing new tools to evaluate pavement performance will benefit from this study by understanding the precision trade-off to recommend best practices in utilizing the connected vehicle method.Item Accuracy Enhancement of Roadway Anomaly Localization Using Connected Vehicles(2016) Bridgelall, Raj; Tolliver, Denver D.; Upper Great Plains Transportation InstituteThe 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.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.