@article{karanam_goenaga_underwood_2023, title={Quantifying Uncertainty with Pavement Performance Models: Comparing Bayesian and Non-Parametric Methods}, volume={3}, ISSN={["2169-4052"]}, DOI={10.1177/03611981231155188}, abstractNote={An important part of pavement management systems is accurately estimating the performance-time-degradation relationship. One common approach to establishing this relationship is to use performance family curves. These curves are developed by collecting performance data at specific points in time and collectively shifting pavements of various ages to identify the probable underlying function. This paper compares two alternative methods for characterizing such a family curve function. First, a Bayesian method (Method-A) is used, which fits both the family curve and the shift factor function in parallel by assuming a Beta distribution for pavement performance condition rating (PCR). Second, a non-parametric method (Method-B) is developed, which fits the model in two steps; (1) by fitting the family; and (2) by horizontal shift to minimize the error. PCR values from flexible pavements in North Carolina (NC-PCR) are used for this comparison. These data include a total of 30,988 pavement sections segregated according to surface type and traffic level. Data from 2013 to 2015 are used for model calibration, and data from 2016 are used for model validation. The root means square error and k-fold cross-validation test are used to conduct the comparison, and Method-A is found to be preferred. The uncertainty in both models is quantified and compared. On the basis of this uncertainty, the Bayesian method is preferred, but in cases with large data sets, a non-parametric method does result in lower uncertainty.}, journal={TRANSPORTATION RESEARCH RECORD}, author={Karanam, Gnana Deepika and Goenaga, Boris and Underwood, Benjamin Shane}, year={2023}, month={Mar} }
 @article{goenaga_karanam_underwood_2022, title={Method to Reduce Uncertainty in the Prediction of Pavement Condition With a Lower Sample Frequency}, volume={4}, ISSN={["2169-4052"]}, DOI={10.1177/03611981221086636}, abstractNote={In this paper, the effect of missing pavement condition observations in the predictions of the future state of a road network was evaluated. Real data from North Carolina were used for this purpose. First, the auto-regression method was compared against the most common “family-curve” modeling approach. It was found that the auto-regression method improves the predictive accuracy of predictions, at both project and network levels. By using the auto-regression method over the “family-curve” approach it is possible to reduce, on average, the Mean Absolute Percent Error of the predictions by 40%. Second, this paper evaluates the case in which a reduced survey frequency is unavoidable, and state highway agencies might need to plan the network maintenance based on historical observations and the subsample of the current condition. Observations of the Pavement Condition Rating for years 2013–2019 were used to define four different scenarios of reduced survey frequency: Scenario 1—“business-as-usual,” where the entire network is surveyed every year; Scenario 2—“reduced-sampling,” analyzed the case where the entire network is surveyed every other year; Scenario 3—“halfway-sampling,” evaluates the case where only half of the network is surveyed every year; and Scenario 4—“least-sampling,” considers the case where only a third of the network is monitored every year. Scenario 1 was used as the baseline of comparison, and as expected it was found that whenever possible the network should be monitored annually; however, if that is not feasible the best option, from the ones evaluated in the paper, should be Scenario 3.}, journal={TRANSPORTATION RESEARCH RECORD}, author={Goenaga, Boris and Karanam, Deepika and Underwood, Benjamin Shane}, year={2022}, month={Apr} }
 @article{goenaga_matini_karanam_underwood_2021, title={Disruption and Recovery: Initial Assessment of COVID-19 Traffic Impacts in North Carolina and Virginia}, volume={147}, ISSN={["2473-2893"]}, DOI={10.1061/JTEPBS.0000518}, abstractNote={AbstractThe coronavirus disease of 2019 (COVID-19) pandemic has affected every aspect of peoples’ lives, including their mobility. In this study, the impact of closures related to the pandemic on t...}, number={4}, journal={JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS}, author={Goenaga, Boris and Matini, Narges and Karanam, Deepika and Underwood, B. Shane}, year={2021}, month={Apr} }