Specification of Obstacles in the Longitudinal Road Profile by Median Filtering

Kropáč, Oldřich; Múčka, Peter

doi:10.1061/(asce)te.1943-5436.0000209

Cited by 12 publications

(9 citation statements)

References 16 publications

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“…To obtain better distinguishing ability of obstacle parameters, the original sections sampling with Δl = 0.1524 m step was re-sampled with 1 cm while applying linear interpolation. At the same time, some further formal provisions were conducted to enable unambiguous estimation of geometric obstacle parameters (for details see [8]).…”

Section: Separation Of Obstacles From the Longitudinal Road Profile Bmentioning

confidence: 99%

“…It must be noted that these LTPP data resulted from the pre-processing of primary data collected at 2.54 cm distance intervals with a 30.48 cm long moving average (MA) filter applied before downsampling to 0.1524 m distance intervals. Due to this pre-processing, especially that of the MA filter, a substantial distortion of the basic obstacle dimensions appeared the quantification of which is presented in [8]. The possible impact on the obstacle shape will be discussed in Section 5.1.…”

Section: Road Data Usedmentioning

confidence: 99%

“…The authors elaborated different partial features of the obstacle problem in papers [6][7][8][9] where references to other authors dealing with this topic are summarized and therefore are not repeated here. Only additional references dealing with the obstacle shapes are cited.…”

Section: Introductionmentioning

confidence: 99%

“…Only additional references dealing with the obstacle shapes are cited. The papers [6][7][8][9] do not solved the problem of the obstacles' shapes. In the papers [6,7], the analytical obstacle shapes (trapezoidal and cosine) and obstacles' dimensions without any connection with the obstacles of the real road profiles were used.…”

Section: Introductionmentioning

confidence: 99%

“…The aim was to estimate the influence of obstacles superimposed on the random road profile on commonly used road unevenness statistical descriptors [6] and the vehicle vibration response [7]. The papers [8,9] describe the separation of obstacles from the continuous random unevenness component using median filtering. Exploiting the LTPP program database [5], statistics and probability density functions (PDFs) of six geometric indicators of obstacles (length, height, ratio length to height, area, volume and distance between obstacles) were evaluated.…”

Section: Introductionmentioning

confidence: 99%

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Shapes of Obstacles in the Longitudinal Road Profile

Kropáč¹,

Múčka

2011

Shock and Vibration

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A voluminous set of longitudinal road profiles gathered from the Long Term Pavement Performance (LTPP) program was processed using median filtering to separate individual large obstacles from the basic quasi-homogeneous random road unevenness. The shapes of 16590 obstacles were analysed partitioned into four sub-groups according to the asphalt-concrete vs. cement-concrete road surfaces, and positive (bumps) vs. negative (potholes) vertical elevations. The empirical obstacle shapes were fitted using seven symmetric and two asymmetric analytical shapes. Based on the root mean square error (RMSE) criterion and accounting for some practical aspects connected with the possible use for the simulation of road profiles with obstacles generalized parabolic shapes (power function) and half-sine shapes seem to be recommendable as adequate approximation functions. Asymmetric obstacle shapes of cosine and parabolic shapes were also briefly discussed and their impact on the vibration transient response of a quarter-car model outlined.

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Section: Separation Of Obstacles From the Longitudinal Road Profile Bmentioning

confidence: 99%

Section: Road Data Usedmentioning

confidence: 99%