ArticleSustainable cities and society2022
ANN-Based traffic volume prediction models in response to COVID-19 imposed measures.
Article in Sustainable cities and society, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Zero-shot traffic flow prediction with foundation models: a comparison with deep learning approaches.Scientific reports · 2026Article
- COVID-19 and campus users: A longitudinal and place-based study of university mobilities in Texas.Sustainable cities and society · 2023Article
- Modeling the resilience of urban mobility when exposed to the COVID-19 pandemic: A qualitative system dynamics approach.Sustainable cities and society · 2023Article
- The impact of traffic control measures on the spread of COVID-19 within urban agglomerations based on a modified epidemic model.Cities (London, England) · 2023Article
- COVID-19 Prediction Using Black-Box Based Pearson Correlation Approach.Diagnostics (Basel, Switzerland) · 2023Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many countries around the globe have imposed several response measures to suppress the rapid spread of the COVID-19 pandemic since the beginning of 2020. These measures have impacted routine daily activities, along with their impact on economy, education, social and recreational activities, and domestic and international travels. Intuitively, the different imposed policies and measures have indirect impacts on urban traffic mobility. As a result of those imposed measures and policies, urban traffic flows have changed. However, those impacts are neither measured nor quantified. Therefore, estimating the impact of these combined yet different policies and measures on urban traffic flows is a challenging task. This paper demonstrates the development of an artificial neural networks (ANN) model which correlates the impact of the imposed response measure and other factors on urban traffic flows. The results show that the adopted ANN model is capable of mapping the complex relationship between traffic flows and the response measures with a high level of accuracy and good performance. The predicted values are closed to the observed ones. They are clustered around the regression line, with a coefficient of determination (
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