Road freight transport forecasting

ǂa ǂfuzzy Monte-Carlo simulation-based model selection approach

Dejan Dragan (Author), Simona Šinko (Author), Abolfazl Keshavarzsaleh (Author), Maja Rosi (Author)

Abstract

As important as the classical approaches such as Akaike's AIC information and Bayesian BIC criterion in model-selection mechanism are, they have limitations. As an alternative, a novel modeling design encompasses a two-stage approach that integrates Fuzzy logic and Monte Carlo simulations (MCSs). In the first stage, an entire family of ARIMA model candidates with the corresponding information-based, residual-based, and statistical criteria is identified. In the second stage, the Mamdani fuzzy model (MFM) is used to uncover interrelationships hidden among previously obtained models criteria. To access the best forecasting model, the MCSs are also used for different settings of weights loaded on the fuzzy rules. The obtained model is developed to predict the road freight transport in Slovenia in the context of choosing the most appropriate electronic toll system. Results show that the mechanism works well when searching for the best model that provides a well-fit to the real data.

Keywords

napovedovanje cestnega transporta;cestni transport;elektronski sistem cestninjenja;simulacija Monte Carlo;modeli ARIMA;logistika;forecasting road transport;electronic toll system;Monte Carlo simulation;ARIMA models;logistics;

Data

Language:	English
Year of publishing:	2022
Typology:	1.01 - Original Scientific Article
Organization:	UM FL - Faculty of Logistics
Publisher:	Strojarski fakultet, Elektrotehnički fakultet, Građevinski fakultet
UDC:	519.2
COBISS:	95822851
ISSN:	1330-3651
Views:	31
Downloads:	0
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Slovenian
Secondary keywords:	napovedovanje cestnega transporta;cestni transport;elektronski sistem cestninjenja;simulacija Monte Carlo;modeli ARIMA;logistika;
Type (COBISS):	Scientific work
Pages:	str. 81-91
Volume:	ǂVol. ǂ29
Issue:	ǂno. ǂ1
Chronology:	2022
DOI:	10.17559/TV-20210110140112
ID:	24181764