Journal: Integrated Computer-Aided Engineering, vol. 28, no. 4, pp. 417-435, 2021
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.