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

被引：2

作者：

Balasubramaniam, Anandkumar ^{[1
]}

Balasubramaniam, Thirunavukarasu ^{[2
,3
]}

Jeyaraj, Rathinaraja ^{[1
]}

Paul, Anand ^{[1
]}

Nayak, Richi ^{[2
,3
]}

机构：

[1] Kyungpook Natl Univ, Sch Comp Sci & Engn, Daegu, South Korea

[2] Queensland Univ Technol, Sch Comp Sci, Brisbane, Qld, Australia

[3] Queensland Univ Technol, Ctr Data Sci, Brisbane, Qld, Australia

来源：

DATA MINING, AUSDM 2021 | 2021年 / 1504卷

基金：

新加坡国家研究基金会;

关键词：

Traffic pattern; NMF; Pattern mining; Spatio-temporal analysis; COVID-19;

D O I：

10.1007/978-981-16-8531-6_16

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：223 / 234

页数：12