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Many cities have issued \"stay-at-home\" orders during 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 a result, urban traffic congestion patterns have changed dramatically, and understanding these changes is crucial for effective emergency traffic management and control efforts. While previous studies have focused on natural disasters or major accidents, only a few have examined pandemic-related traffic congestion patterns. This paper uses correlations and machine learning techniques to analyze the relationship between COVID-19 and transportation. The authors simulated traffic models for five different networks and proposed a Traffic Prediction Technique (TPT), which includes an Impact Calculation Methodology that uses Pearson's Correlation Coefficient and Linear Regression, as well as a Traffic Prediction 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 and transportation patterns, and the CNN has a high accuracy rate in predicting these impacts.<\/jats:p>","DOI":"10.1007\/s11042-023-15803-1","type":"journal-article","created":{"date-parts":[[2023,6,7]],"date-time":"2023-06-07T04:34:31Z","timestamp":1686112471000},"page":"7295-7316","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Exploring the effects of pandemics on transportation through correlations and deep learning techniques"],"prefix":"10.1007","volume":"83","author":[{"given":"Samah A.","family":"Gamel","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Esraa","family":"Hassan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nora","family":"El-Rashidy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6116-2191","authenticated-orcid":false,"given":"Fatma M.","family":"Talaat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,6,7]]},"reference":[{"key":"15803_CR1","unstructured":"1995\u20132019: U.S. Department of Transportation, Federal Highway Administration, Highway Statistics (Washington, DC: Annual Issues), table VM-1, available at http:\/\/www.fhwa.dot.gov\/policyinformation\/statistics.cfm as of Jan. 8, 2021"},{"key":"15803_CR2","doi-asserted-by":"publisher","unstructured":"Albawi S, Mohammed TA, Al-Zawi S (2017) Understanding of a convolutional neural network. 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