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Statistical modeling of cargo securing on selected military trucks and road surfaces

Author

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  • Jiri Neubauer
  • Martin Vlkovsky
  • Jaroslav Michalek

Abstract

The development of new technologies (e.g., three-axial accelerometers) and their implementation to the armed forces made it possible to rather easily obtain data on transportation with regard to cargo securing. It is, however, crucial to evaluate the data, and identify the factors important for the selection of an appropriate cargo securing system. Particular attention should be paid to the transportation on low-quality roads, or possibly in the terrain as it can be expected that the cargo (as well as the vehicle and the driver) is subjected to greater shocks. Data obtained from the military transport experiment have been processed using advanced statistical methods (a contaminated probability distribution, methods of statistical comparison, and an analysis of variance). The transportation data from the use of two military trucks on three different road surfaces has been compared. The results show, in which axes there are statistically significant differences in terms of the frequency of exceeding normatively determined acceleration coefficient values in relation to the road surface type and the vehicle type. Further results are based on the modeling of acceleration coefficients using a contaminated log-normal distribution, where the distribution of acceleration coefficient values is contaminated by outliers resulting from sudden changes in the transport conditions.

Suggested Citation

  • Jiri Neubauer & Martin Vlkovsky & Jaroslav Michalek, 2024. "Statistical modeling of cargo securing on selected military trucks and road surfaces," The Journal of Defense Modeling and Simulation, , vol. 21(3), pages 341-355, July.
  • Handle: RePEc:sae:joudef:v:21:y:2024:i:3:p:341-355
    DOI: 10.1177/15485129241227012
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    References listed on IDEAS

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    1. Ankit Shah & Katheryn A. Farris & Rajesh Ganesan & Sushil Jajodia, 2022. "Vulnerability Selection for Remediation: An Empirical Analysis," The Journal of Defense Modeling and Simulation, , vol. 19(1), pages 13-22, January.
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