TY - GEN
T1 - Nonnegative Matrix Factorization to Understand Spatio-Temporal Traffic Pattern Variations During COVID-19
T2 - 19th Australasian Conference on Data Mining, AusDM 2021
AU - Balasubramaniam, Anandkumar
AU - Balasubramaniam, Thirunavukarasu
AU - Jeyaraj, Rathinaraja
AU - Paul, Anand
AU - Nayak, Richi
N1 - Publisher Copyright:
© 2021, Springer Nature Singapore Pte Ltd.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - COVID-19
KW - NMF
KW - Pattern mining
KW - Spatio-temporal analysis
KW - Traffic pattern
UR - http://www.scopus.com/inward/record.url?scp=85121929102&partnerID=8YFLogxK
U2 - 10.1007/978-981-16-8531-6_16
DO - 10.1007/978-981-16-8531-6_16
M3 - Conference contribution
AN - SCOPUS:85121929102
SN - 9789811685309
T3 - Communications in Computer and Information Science
SP - 223
EP - 234
BT - Data Mining - 19th Australasian Conference on Data Mining, AusDM, 2021, Proceedings
A2 - Xu, Yue
A2 - Wang, Rosalind
A2 - Lord, Anton
A2 - Boo, Yee Ling
A2 - Nayak, Richi
A2 - Zhao, Yanchang
A2 - Williams, Graham
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 14 December 2021 through 15 December 2021
ER -