A Nonparametric Approach to Road Safety Analysis - Does It Make a Difference?

Thakali, Lalita; Fu, Liping; Chen, Tao; Usman, Taimur

doi:10.1061/9780784413036.087

Ictis 2013 2013

DOI: 10.1061/9780784413036.087

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A Nonparametric Approach to Road Safety Analysis - Does It Make a Difference?

Lalita Thakali

Liping Fu

Tao Chen

et al.

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2023

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“…In this sense, given the data at hand replicates from the same distribution can be obtained by sampling with replacement (to preserve the probability density function) from the data itself. It must be noted that the non-parametric method is not an estimation approach per se, rather is a technique to generate random samples from a given distribution of data thus obtaining estimates of prediction intervals and parameter uncertainties ( 15 ).…”

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confidence: 99%

Quantifying Uncertainty with Pavement Performance Models: Comparing Bayesian and Non-Parametric Methods

Karanam

Goenaga

Underwood

2023

Transportation Research Record: Journal of the Transportation R

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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.

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mentioning

confidence: 99%

Quantifying Uncertainty with Pavement Performance Models: Comparing Bayesian and Non-Parametric Methods

Karanam

Goenaga

Underwood

2023

Transportation Research Record: Journal of the Transportation R

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A Nonparametric Approach to Road Safety Analysis - Does It Make a Difference?

Cited by 1 publication

References 13 publications

Quantifying Uncertainty with Pavement Performance Models: Comparing Bayesian and Non-Parametric Methods

Quantifying Uncertainty with Pavement Performance Models: Comparing Bayesian and Non-Parametric Methods

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