ANN-Based traffic volume prediction models in response to COVID-19 imposed measures. (June 2022)

• Record Type: Journal Article
• Authors: Ghanim, Mohammad Shareef; Muley, Deepti; Kharbeche, Mohamed
• Is Part Of: Sustainable cities and society. Volume 81(2022)
• Publication Date: 2022-06
• Subjects: COVID-19 -- Mobility Impact -- Pandemic Preventive Measures -- Traffic Mobility -- Machine Learning -- Prediction Model -- State of Qatar; Sustainable urban development -- Periodicals; Sustainable buildings -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.76
• DOI: 10.1016/j.scs.2022.103830
• Languages: English
• ISSNs: 2210-6707

Abstract

Highlights: Many of the imposed measures to reduce the spread of COVID-19 pandemic have impacted urban traffic. An Artificial Neural Network (ANN) model is developed to predict average daily traffic for urban key junctions. The ANN model uses seasonal factors and imposed measures as inputs. The ANN model performs well in predicting average daily traffic at key junctions. The developed model can be utilized to support urban traffic prediction during special events or emergency situations. 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 ( R 2 ) 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.