Public Transit Prediction during COVID-19 Pandemic

Smith, J.; Zhu, Y.; Li, Z.

Smith, J.; Zhu, Y.; Li, Z..

8th IEEE International Conference on Big Data Security on Cloud, 8th IEEE International Conference on High Performance and Smart Computing, and 8th IEEE International Conference on Intelligent Data and Security, BigDataSecurity/HPSC/IDS 2022 ; : 92-94, 2022.

Article in English | Scopus | ID: covidwho-1961365

ABSTRACT

ABSTRACT

Public transit demand is an import indicator of economic and social activity level. To accurately predict the public transport demand change during the COVID pandemic, in this paper, we investigate various factors affecting such demand change and collect related data from multiple sources. Different prediction models including linear regression and deep neural networks are explored. Experiments were conducted and the results show that though COVID-19 pandemic greatly affect the public transport, our proposed approach can accurately predict the next day public transit volume. © 2022 IEEE.

Keywords

COVID Pandemic; Neural Network; Prediction; Public Transport Demand; Deep neural networks; Forecasting; Urban transportation; Demand changes; Economic activities; Neural-networks; Public transit; Public transport; Social activities; Transit demand; Transport demand; COVID-19

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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Prognostic study Language: English Journal: HPSC Year: 2022 Document Type: Article

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