Road freight transport forecasting

ǂa ǂfuzzy Monte-Carlo simulation-based model selection approach

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

Povzetek

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.

Ključne besede

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;

Podatki

Jezik:	Angleški jezik
Leto izida:	2022
Tipologija:	1.01 - Izvirni znanstveni članek
Organizacija:	UM FL - Fakulteta za logistiko
Založnik:	Strojarski fakultet, Elektrotehnički fakultet, Građevinski fakultet
UDK:	519.2
COBISS:	95822851
ISSN:	1330-3651
Št. ogledov:	31
Št. prenosov:	0
Ocena:	0 (0 glasov)
Metapodatki:

Ostali podatki

Sekundarni jezik:	Slovenski jezik
Sekundarne ključne besede:	napovedovanje cestnega transporta;cestni transport;elektronski sistem cestninjenja;simulacija Monte Carlo;modeli ARIMA;logistika;
Vrsta dela (COBISS):	Znanstveno delo
Strani:	str. 81-91
Letnik:	ǂVol. ǂ29
Zvezek:	ǂno. ǂ1
Čas izdaje:	2022
DOI:	10.17559/TV-20210110140112
ID:	24181764