Show simple item record

AuthorMohammad Shareef, Ghanim
AuthorMuley, Deepti
AuthorKharbeche, Mohamed
Available date2022-05-15T07:05:25Z
Publication Date2022-06-30
Publication NameSustainable Cities and Society
Identifierhttp://dx.doi.org/10.1016/j.scs.2022.103830
CitationGhanim, M. S., Muley, D., & Kharbeche, M. (2022). ANN-Based traffic volume prediction models in response to COVID-19 imposed measures. Sustainable cities and society, 81, 103830.
ISSN22106707
URIhttps://www.sciencedirect.com/science/article/pii/S2210670722001573
URIhttp://hdl.handle.net/10576/30869
AbstractMany 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 (R2) of 0.9761. Furthermore, the developed model can be generalized to determine the anticipated demand levels resulted from imposing any of the response measures in the post-pandemic era. This model can be used to manage traffic during mega-events. It can be also utilized for disaster or emergency situations, where traffic flow estimates are highly required for operational and planning purposes.
Languageen
PublisherElsevier
SubjectCOVID-19
Mobility Impact
Pandemic Preventive Measures
Traffic Mobility
Machine Learning
Prediction Model
State of Qatar
TitleANN-Based traffic volume prediction models in response to COVID-19 imposed measures
TypeArticle
Volume Number81
dc.accessType Abstract Only


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record