scholarly journals Exploring Multiple‐discreteness in Freight Transport. A Multiple Discrete Extreme Value Model Application for Grain Consolidators in Argentina

2021 ◽  
Author(s):  
Rodrigo J. Tapia ◽  
Gerard de Jong ◽  
Ana M. Larranaga ◽  
Helena B. Bettella Cybis
Keyword(s):  
Utility Function ◽  
Stated Preference ◽  
Freight Transport ◽  
Extreme Value ◽  
Random Coefficients ◽  
Transport Cost ◽  
Data Set ◽  
Extreme Value Model ◽  
Value Model ◽  
Multiple Discreteness

AbstractThere are some examples where freight choices may be of a multiple discrete nature, especially the ones at more tactical levels of planning. Nevertheless, this has not been investigated in the literature, although several discrete-continuous models for mode/vehicle type and shipment size choice have been developed in freight transport. In this work, we propose that the decision of port and mode of the grain consolidators in Argentina is of a discrete-continuous nature, where they can choose more than one alternative and how much of their production to send by each mode. The Multiple Discrete Extreme Value Model (MDCEV) framework was applied to a stated preference data set with a response variable that allowed this multiple-discreteness. To our knowledge, this is the only application of the MDCEV in regional freight context. Free alongside ship price, freight transport cost, lead-time and travel time were included in the utility function and observed and random heterogeneity was captured by the interaction with the consolidator’s characteristics and random coefficients. In addition, different discrete choice models were used to compare the forecasting performance, willingness to pay measures and structure of the utility function against.

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.

scholarly journals Characteristics Analysis of Freight Mode Choice Model According to the Introduction of a New Freight Transport System

2019 ◽  
Vol 11 (4) ◽  
pp. 1209 ◽  
Author(s):  
Seungjin Shin ◽  
Hong-Seung Roh ◽  
Sung Hur
Keyword(s):  
Travel Time ◽  
Transport System ◽  
Choice Model ◽  
Stated Preference ◽  
Statistical Significance ◽  
Explanatory Power ◽  
Service Level ◽  
Freight Transport ◽  
Transport Cost ◽  
Transport Mode

The purpose of this study is to identify the characteristics of freight mode choices made by shippers and carriers with the introduction of a new freight transport system. We set an area in which actual freight transport takes place as the analysis scope and performed a survey of the shippers and carriers that transport containers to identify their stated preference (SP) regarding the new freight mode. The SP survey was carried out through an experimental design and this study considered the three factors of transport time, transport cost, and service level. This study compared and analyzed the models by distance using an individual behavior model. The results of estimating the model showed that the explanatory power of the model classified by distance and the individual parameters have statistical significance. The hit ratio was also high, which confirms that the model was estimated properly. In addition, the range of elasticity and the value of travel time analyzed using the model were evaluated to be appropriate compared to previous studies. The findings of the elasticity analysis show that strategies for reducing the transport cost are effective to increase the demand for the new transport mode. The value of travel time of freight transport was found to be higher than the current value generally applied in Korea. Considering that the value of travel time currently used is based on road freight transport, further research is required to apply a new value of travel time that reflects the characteristics of the new transport mode in the future.

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>

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