Technical gazette, Vol. 29 No. 1, 2022.
Original scientific paper
https://doi.org/10.17559/TV-20210110140112
Road Freight Transport Forecasting: A Fuzzy Monte-Carlo Simulation-Based Model Selection Approach
Dejan Dragan*
; Faculty of logistics, University of Maribor, Mariborska 7, Celje, Slovenia
Simona Šinko
; Faculty of logistics, University of Maribor, Mariborska 7, Celje, Slovenia
Abolfazl Keshavarzsaleh
; Faculty of Business, Inter. University of Malaya-Wales, 50480 Kuala Lumpur, Malaysia
Maja Rosi
; Faculty of logistics, University of Maribor, Mariborska 7, Celje, Slovenia
Abstract
As important as the classical approaches such as Akaikeꞌs 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 modelsꞌ 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
ARIMA models; forecasting road transport; Mamdani fuzzy inference model; Monte Carlo simulations; time series model selection
Hrčak ID:
269486
URI
Publication date:
15.2.2022.
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