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Addressing Uncertainty by Designing an Intelligent Fuzzy System to Help Decision Support Systems for Winter Road Maintenance
Department of Industrial Engineering, UiT/The Arctic University of Norway, 8514 Narvik, Nordland, Norway.ORCID iD: 0000-0002-6118-3299
Department of Industrial Engineering, UiT/The Arctic University of Norway, 8514 Narvik, Nordland, Norway.
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Fluid and Experimental Mechanics.ORCID iD: 0000-0001-8225-989X
2022 (English)In: Safety, E-ISSN 2313-576X, Vol. 8, no 1, article id 14Article in journal (Refereed) Published
Abstract [en]

One of the main challenges in developing efficient and effective winter road maintenance is to design an accurate prediction model for the road surface friction coefficient. A reliable and accurate prediction model of road surface friction coefficient can help decision support systems to significantly increase traffic safety, while saving time and cost. High dynamicity in weather and road surface conditions can lead to the presence of uncertainties in historical data extracted by sensors. To overcome this issue, this study uses an adaptive neuro-fuzzy inference system that can appropriately address uncertainty using fuzzy logic neural networks. To investigate the ability of the proposed model to predict the road surface friction coefficient, real data were measured at equal time intervals using optical sensors and road-mounted sensors. Then, the most critical features were selected based on the Pearson correlation coefficient, and the dataset was split into two independent training and test datasets. Next, the input variables were fuzzified by generating a fuzzy inference system using the fuzzy c-means clustering method. After training the model, a testing set was used to validate the trained model. The model was evaluated by means of graphical and numerical metrics. The results show that the constructed adaptive neuro-fuzzy model has an excellent ability to learn and accurately predict the road surface friction coefficient.

Place, publisher, year, edition, pages
MDPI, 2022. Vol. 8, no 1, article id 14
Keywords [en]
adaptive neuro-fuzzy inference system (ANFIS), prediction methods, road surface friction, road transportation safety, winter road maintenance
National Category
Geotechnical Engineering Computer Sciences
Research subject
Experimental Mechanics
Identifiers
URN: urn:nbn:se:ltu:diva-89874DOI: 10.3390/safety8010014ISI: 000774293600001Scopus ID: 2-s2.0-85125137821OAI: oai:DiVA.org:ltu-89874DiVA, id: diva2:1647158
Note

Validerad;2022;Nivå 2;2022-03-25 (hanlid);

Funder: Ministry of Education and Research, Norway (470079)

Available from: 2022-03-25 Created: 2022-03-25 Last updated: 2022-11-02Bibliographically approved

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Casselgren, Johan

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