Exploiting COVID-19 related traffic changes to evaluate flow dependency of an FCD-defined congestion measure

Abstract

Traffic congestion poses a significant problem in urban areas globally, and yet no measure of congestion is universally applied. Various studies have evaluated congestion measures, however, none have identified and demonstrated a best-practice congestion metric that can compare congestion between road segments, networks, and city zones. Furthermore, no studies have proven the link between a congestion metric and traffic flow, despite suggestion by Lomax et al. in their seminal 1997 Quantifying Congestion report that an appropriate congestion metric should vary predictably according to flow. This paper aims to address these gaps in traffic congestion literature. Various congestion measures are evaluated according to standard criteria. Although this process has been followed before, this paper adds a unique criterion that requires congestion to be quantifiable from commercial floating car data (FCD), due to its extensive availability and relative affordability. The most appropriate congestion measure is evaluated to be the speed reduction index (SRI). The application of the SRI to describe spatiotemporal congestion patterns and flow dependency is then demonstrated in a case-study analysis in South Africa. This analysis exploited traffic impacts of the COVID-19 pandemic in 2020 (particularly, the stepwise increase from severely reduced traffic flows as lockdown levels eased), to evaluate SRI. The wide range of flows enabled an unprecedented regression analysis comparing congestion level and flow. The results of the regression analysis are highly significant (p < 0.001) indicating that SRI-based congestion measurement tracks flow variation. This study further identified that unidirectional congestion, quantified by the SRI, is impacted by high bi-directional flow along arterials. These findings confirm the appropriateness of the SRI quantified from commercial FCD to measure congestion.