首页|Exploring the effects of pandemics on transportation through correlations and deep learning techniques
Exploring the effects of pandemics on transportation through correlations and deep learning techniques
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NETL
NSTL
Springer Nature
The COVID-19 pandemic has had a significant impact on human migration worldwide,affecting transportation patterns in cities. Many cities have issued "stay-at-home" ordersduring the outbreak, causing commuters to change their usual modes of transportation.For example, some transit/bus passengers have switched to driving or car-sharing. As aresult, urban traffic congestion patterns have changed dramatically, and understandingthese changes is crucial for effective emergency traffic management and control efforts.While previous studies have focused on natural disasters or major accidents, only a fewhave examined pandemic-related traffic congestion patterns. This paper uses correlationsand machine learning techniques to analyze the relationship between COVID-19 and transportation.The authors simulated traffic models for five different networks and proposeda Traffic Prediction Technique (TPT), which includes an Impact Calculation Methodologythat uses Pearson’s Correlation Coefficient and Linear Regression, as well as a TrafficPrediction Module (TPM). The paper’s main contribution is the introduction of the TPM,which uses Convolutional Neural Network to predict the impact of COVID-19 on transportation.The results indicate a strong correlation between the spread of COVID-19 andtransportation patterns, and the CNN has a high accuracy rate in predicting these impacts.
NETL
NSTL
Springer Nature
