scholarly journals A escolha de não ser discreto: discutindo a utilização de modelos discretos contínuos no transporte de carga

TRANSPORTES ◽  
2020 ◽  
Vol 28 (4) ◽  
pp. 64-75
Author(s):  
Rodrigo Javier Tapia ◽  
Ana Margarita Larranaga ◽  
Helena Beatriz Cybis ◽  
Gerard De Jong
Keyword(s):  
Extreme Value ◽  
Extreme Value Model ◽  
Value Model

O transporte de carga tem utilizado os modelos herdados do transporte de passageiros e com eles todos seus pressupostos tradicionais. Mas são todos eles válidos? O presente artigo visa discutir sobre um dos pressupostos menos contestados neste processo de modelagem: o da exclusividade mútua das alternativas no contexto do transporte de carga. Para isso, este artigo apresenta uma aplicação de Multiple Discrete Extreme Value Model (MDCEV) para a escolha de modo e porto para os consolidadores de grãos na Argentina. O modelo é desenvolvido a partir de uma pesquisa de Preferência Declarada que permitia a escolha de mais de uma alternativa simultaneamente. A escolha é descrita pelo Tempo de Viagem, Tempo de Espera do Serviço, Preço de venda no porto e Custo do Frete. O MDCEV permitiu obter informação sobre o efeito da saciedade das diferentes alternativas. De esta maneira, o MDCEV pode ser uma ferramenta valiosa para a modelagem de escolhas táticas e estratégicas.

A Bivariate Extreme Value Model for Estimating Crash Frequency by Severity using Traffic Conflicts

2021 ◽  
pp. 100180
Author(s):  
Ashutosh Arun ◽  
Md. Mazharul Haque ◽  
Ashish Bhaskar ◽  
Simon Washington ◽  
Tarek Sayed
Keyword(s):  
Extreme Value ◽  
Crash Frequency ◽  
Traffic Conflicts ◽  
Extreme Value Model ◽  
Value Model

An Extreme Value Model for U.S. Hail Size

2017 ◽  
Vol 145 (11) ◽  
pp. 4501-4519 ◽  
Author(s):  
John T. Allen ◽  
Michael K. Tippett ◽  
Yasir Kaheil ◽  
Adam H. Sobel ◽  
Chiara Lepore ◽  
...  
Keyword(s):  
Gumbel Distribution ◽  
The United States ◽  
Value Theory ◽  
Extreme Value ◽  
Extreme Value Distributions ◽  
The Past ◽  
Return Levels ◽  
Data Limitations ◽  
Extreme Value Model ◽  
Value Model

The spatial distribution of return intervals for U.S. hail size is explored within the framework of extreme value theory using observations from the period 1979–2013. The center of the continent has experienced hail in excess of 5 in. (127 mm) during the past 30 yr, whereas hail in excess of 1 in. (25 mm) is more common in other regions, including the West Coast. Observed hail sizes show heavy quantization toward fixed-diameter reference objects and are influenced by spatial and temporal biases similar to those noted for hail occurrence. Recorded hail diameters have been growing in recent decades because of improved reporting. These data limitations motivate exploration of extreme value distributions to represent the return periods for various hail diameters. The parameters of a Gumbel distribution are fit to dithered observed annual maxima on a national 1° × 1° grid at locations with sufficient records. Gridded and kernel-smoothed return sizes and quantiles up to the 200-yr return period are determined for the fitted Gumbel distribution. These results are used to illustrate return levels for hail greater than a given size for at least one location within each 1° × 1° grid box for the United States.

Towards a reproducible snow load map – an example for Austria

2021 ◽  
Author(s):  
Harald Schellander ◽  
Michael Winkler ◽  
Tobias Hell
Keyword(s):  
Additive Model ◽  
Penalized Regression ◽  
Extreme Value ◽  
Ease Of Use ◽  
Return Level ◽  
Snow Load ◽  
Snow Model ◽  
Penalized Regression Splines ◽  
Extreme Value Model ◽  
Value Model

<p>The European Committee for Standardization provides coarse rules for the estimation of snow load maps for structural design. European countries can apply their own methodologies, resulting in inconsistencies for the 50-year return level of snow load at national borders. Commonly used approaches base on more or less sophisticated interpolation of snow depths with a subsequent assignment of snow density, or spatial extreme value interpolation of snow load measurements.  </p><p>We propose a novel methodology for Austria, where snow load observations are not available. It is based on (1) modeling yearly snow load maxima with the specially developed ∆SNOW model, and (2) a generalized additive model, where explaining covariates and their combinations are represented by penalized regression splines, fitted to such derived snow load series. Results show an RMSE of 0.7 kN/m<sup>2</sup>, and a BIAS of -0.2 kN/m<sup>2</sup> over all altitudes, thereby outperforming a smooth spatial extreme value model and the actual Austrian standard, when compared to locally estimated, “quasi-observed “ 50-year snow load maxima at 870 stations in and tightly around Austria.</p><p>The new approach requires no zoning and provides a reproducible and transparent approach. Due to the relatively ease of use and snow depth measurements as single prerequisite, the method is applicable in other countries as well. Negative BIASes, that significantly underestimate 50-year snow loads at a small number of stations, are the only objective problem that has to be solved before the new map can be proposed as a successor of the actual Austrian snow load map.</p>

scholarly journals Evaluation of classical spatial-analysis schemes of extreme rainfall

2012 ◽  
Vol 12 (11) ◽  
pp. 3229-3240 ◽  
Author(s):  
D. Ceresetti ◽  
E. Ursu ◽  
J. Carreau ◽  
S. Anquetin ◽  
J. D. Creutin ◽  
...  
Keyword(s):  
Cross Validation ◽  
Extreme Rainfall ◽  
Value Theory ◽  
Extreme Value ◽  
Optimal Interpolation ◽  
Peaks Over Threshold ◽  
Model Block ◽  
Block Maxima ◽  
Extreme Value Model ◽  
Value Model

Abstract. Extreme rainfall is classically estimated using raingauge data at raingauge locations. An important related issue is to assess return levels of extreme rainfall at ungauged sites. Classical methods consist in interpolating extreme-value models. In this paper, such methods are referred to as regionalization schemes. Our goal is to evaluate three classical regionalization schemes. Each scheme consists of an extreme-value model (block maxima, peaks over threshold) taken from extreme-value theory plus a method to interpolate the parameters of the statistical model throughout the Cévennes-Vivarais region. From the interpolated parameters, the 100-yr quantile level can be estimated over this whole region. A reference regionalization scheme is made of the couple block maxima/kriging, where kriging is an optimal interpolation method. The two other schemes differ from the reference by replacing either the extreme-value model block maxima by peaks over threshold or kriging by a neural network interpolation procedure. Hyper-parameters are selected by cross-validation and the three regionalization schemes are compared by double cross-validation. Our evaluation criteria are based on the ability to interpolate the 100-yr return level both in terms of precision and spatial distribution. It turns out that the best results are obtained by the regionalization scheme combining the peaks-over-threshold method with kriging.

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