Risk models based on uncertainty quantification for illicit traffic time series in customs context Online publication date: Wed, 02-Feb-2022
by Lamia Hammadi; Eduardo Souza De Cursi; Vlad Stefan Barbu; Abdellah Ait Ouahman
International Journal of Shipping and Transport Logistics (IJSTL), Vol. 14, No. 1/2, 2022
Abstract: Due to the increase of the worldwide trade, the flow of goods crossing borders is increasing; at the same time, the threats faced by society are growing. Customs are encountering many challenges and looking for new tools of decision making for countering such risks. This paper develops models based on uncertainty quantification to analyse the behaviour of the risk time series in customs. We start by introducing the Hilbertian approach related to the representation of random variables and addressing these approximations and their applications in UQ. Then we discuss an extension where these models are applied to handle the seasonal components of risks. The models are fitted to the seized quantities of the illicit traffic on five sites using moment matching method. The results provide a good description of important properties of the data and a tool of decisions making on risk analysis in cases of threats in global supply chain.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Shipping and Transport Logistics (IJSTL):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook