Public Transit Prediction During COVID-19 Pandemic

Joshua Smith, Yingying Zhu,Zheng Li

2022 IEEE 8th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS)（2022）

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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.

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Key words

COVID Pandemic,Public Transport Demand,Prediction,Neural Network

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