Nonnegative Matrix Factorization to understand Spatio-Temporal Traffic Pattern Variations during COVID-19: A Case Study

TL;DR

This study uses nonnegative matrix factorization to analyze and compare spatio-temporal traffic patterns in Great Britain before and during COVID-19, revealing behavioral changes crucial for traffic management during pandemics.

Contribution

It introduces a novel application of NMF to understand traffic pattern variations during COVID-19, providing insights for transportation planning in pandemic scenarios.

Findings

Identified significant changes in traffic patterns during COVID-19

Demonstrated NMF's effectiveness in analyzing traffic data

Provided data-driven insights for traffic management strategies

Abstract

Due to the rapid developments in Intelligent Transportation System (ITS) and increasing trend in the number of vehicles on road, abundant of road traffic data is generated and available. Understanding spatio-temporal traffic patterns from this data is crucial and has been effectively helping in traffic plannings, road constructions, etc. However, understanding traffic patterns during COVID-19 pandemic is quite challenging and important as there is a huge difference in-terms of people's and vehicle's travel behavioural patterns. In this paper, a case study is conducted to understand the variations in spatio-temporal traffic patterns during COVID-19. We apply nonnegative matrix factorization (NMF) to elicit patterns. The NMF model outputs are analysed based on the spatio-temporal pattern behaviours observed during the year 2019 and 2020, which is before pandemic and during pandemic situations respectively, in Great Britain. The outputs of the analysed spatio-temporal traffic pattern variation behaviours will be useful in the fields of traffic management in Intelligent Transportation System and management in various stages of pandemic or unavoidable scenarios in-relation to road traffic.