PREDICTING TRAFFIC CONGESTION DURING COVID19 USING HUMAN MOBILITY AND STREET-WASTE FEATURES

Abstract. With COVID-19’s prevalence and government efforts to curb its spread, urban travel behaviour has significantly altered, resulting in a significant shift in traffic congestion. Rather than predicting traffic congestion based on historical data, we aim to model the correlation between travel behaviour and external mobility-related urban features and use Dublin in Ireland as a case study. This study incorporates four categories of urban data, including 1) Mobility-based features, including the government’s interventions and mobility pattern changes in different locations, 2) Environmental features such as weather and urban street-waste, 3) COVID-19- related features such as the positivity and vaccination rates, and 4) Time-related features such as public holidays. First, we examine the impact of COVID-19 on traffic congestion and street-waste to understand the city’s dynamic. Then, multiple machine learning (ML) models, such as random forests, support vector regression, light gradient boosting machine, and multiple linear regression are trained, and their performance optimized to predict traffic congestion changes. We compare the outcomes of the models with several evaluation metrics and interpret the best performing model. The results indicate that mobility changes in grocery and pharmacy, retail and recreation, workplaces sectors, and the amount of urban street-waste significantly contribute to the model outcomes. Findings could predict traffic dynamics in times of crisis and allow authorities to comprehend the effects of their intervention measures on mobility, which would ultimately benefit developing smart cities and intelligent transportation systems.