Addressing Uncertainty by Designing an Intelligent Fuzzy System to Help Decision Support Systems for Winter Road Maintenance
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Department of Industrial Engineering, UiT/The Arctic University of Norway, 8514 Narvik, Nordland, Norway
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Department of Engineering Sciences and Mathematics, Luleå University of Technology, 971 87 Luleå, Sweden
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Author to whom correspondence should be addressed.
Academic Editors: Amir Khorram-Manesh and Tom Brijs
Safety 2022, 8(1), 14; https://doi.org/10.3390/safety8010014
Received: 1 November 2021
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Revised: 26 January 2022
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Accepted: 15 February 2022
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Published: 17 February 2022
(This article belongs to the Topic Multiagency Approach to Disaster Management, Focusing on Triage, Treatment and Transport)
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.
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Keywords:
adaptive neuro-fuzzy inference system (ANFIS); prediction methods; road surface friction; road transportation safety; winter road maintenance
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MDPI and ACS Style
Hatamzad, M.; Polanco Pinerez, G.; Casselgren, J. Addressing Uncertainty by Designing an Intelligent Fuzzy System to Help Decision Support Systems for Winter Road Maintenance. Safety 2022, 8, 14. https://doi.org/10.3390/safety8010014
AMA Style
Hatamzad M, Polanco Pinerez G, Casselgren J. Addressing Uncertainty by Designing an Intelligent Fuzzy System to Help Decision Support Systems for Winter Road Maintenance. Safety. 2022; 8(1):14. https://doi.org/10.3390/safety8010014
Chicago/Turabian StyleHatamzad, Mahshid, Geanette Polanco Pinerez, and Johan Casselgren. 2022. "Addressing Uncertainty by Designing an Intelligent Fuzzy System to Help Decision Support Systems for Winter Road Maintenance" Safety 8, no. 1: 14. https://doi.org/10.3390/safety8010014
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