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• W3184178144 abstract "This paper presents a novel methodology using classification for day-ahead traffic prediction. It addresses the research question whether traffic state can be forecasted based on meteorological conditions, seasonality, and time intervals, as well as COVID-19 related restrictions. We propose reliable models utilizing smaller data partitions. Apart from feature selection, we incorporate new features related to movement restrictions due to COVID-19, forming a novel data model. Our methodology explores the desired training subset. Results showed that various models can be developed, with varying levels of success. The best outcome was achieved when factoring in all relevant features and training on a proposed subset. Accuracy improved significantly compared to previously published work." @default.
• W3184178144 created "2021-08-02" @default.
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• W3184178144 date "2021-08-27" @default.
• W3184178144 modified "2023-09-26" @default.
• W3184178144 title "A methodology using classification for traffic prediction: Featuring the impact of COVID-19" @default.
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• W3184178144 doi "https://doi.org/10.3233/ica-210663" @default.
• W3184178144 hasPublicationYear "2021" @default.
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