A methodology using classification for traffic prediction: Featuring the impact of COVID-19

Issue title: Selection of papers from the 21st EANN (Engineering Applications of Neural Networks) and 16th AIAI (Artificial Intelligence Applications and Innovations) Joint International Conference

Guest editors: Lazaros Iliadis

Article type: Research Article

Authors: Liapis, Stergios^a | Christantonis, Konstantinos^a | Chazan-Pantzalis, Victor^b | Manos, Anastassios^b | Elizabeth Filippidou, Despina^b | Tjortjis, Christos^{a; *}

Affiliations: [a] International Hellenic University, Moudania, Thermi, | [b] DOTSOFT SA, Thessaloniki,

Correspondence: [*] Corresponding author: Christos Tjortjis, School of Science and Technology, International Hellenic University, 14th km Thessaloniki, Moudania, 57001 Thermi, Greece. Tel.: +30 23 1080 7576; Fax: +30 23 1047 4590; E-mail: [email protected].

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.

Keywords: Traffic prediction, classification, data mining, smart cities, COVID-19

DOI: 10.3233/ICA-210663

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