EconPapers    
Economics at your fingertips  
 

On probability distributions of the operational law of container liner ships

Yunting Song and Nuo Wang

Journal of the Royal Statistical Society Series A, 2019, vol. 182, issue 3, 943-961

Abstract: The probability distribution of the operational law of container liner ships in port is the significant theoretical basis for the study of container liners and ports, but there has been a lack of corresponding statistical analysis and theoretical research on that distribution since maritime container transportation came into being. The main purpose of this paper is to identify probabilistic models of the operational law of container liners by statistically analysing the operation data collected from Dalian, Kaohsiung and Rotterdam ports. The results demonstrate that both the interarrival time and the handling time of container liner ships follow higher order Erlang distributions. Under the combined influence of schedule constraints and the interference of some uncertain factors, container liners run between randomness and certainty, presenting the same feature as higher order Erlang distributions. In addition, the world container trade in container ports is subject to similar operational rules and time limits, so it is reasonable to deduce that the above conclusion could be generalized to other container ports. Finally, this paper quantitatively evaluates the degree of port congestion under various probabilistic models, which shows that the study not only has theoretical significance but also values in application.

Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://doi.org/10.1111/rssa.12442

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssa:v:182:y:2019:i:3:p:943-961

Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-985X

Access Statistics for this article

Journal of the Royal Statistical Society Series A is currently edited by A. Chevalier and L. Sharples

More articles in Journal of the Royal Statistical Society Series A from Royal Statistical Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().

 
Page updated 2025-03-19
Handle: RePEc:bla:jorssa:v:182:y:2019:i:3:p:943-961