Dragan, Dejan and Sinko, Simona and Keshavarzsaleh, Abolfazl and Rosi, Maja (2022) Road freight transport forecasting: A fuzzy Monte-Carlo simulation-based model selection approach. Tehnicki Vjesnik-Technical Gazette, 29 (1). pp. 81-91. ISSN 1330-3651, DOI https://doi.org/10.17559/TV-20210110140112.
Full text not available from this repository.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.
| Item Type: | Article |
|---|---|
| Funders: | None |
| Uncontrolled Keywords: | ARIMA models; Forecasting road transport; Mamdani fuzzy inference model; Monte Carlo simulations; Time series model selection |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 04 Aug 2022 03:13 |
| Last Modified: | 04 Aug 2022 03:13 |
| URI: | http://eprints.um.edu.my/id/eprint/33400 |
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