Citation

Paramasivan K, Sudarsanam N. Int. J. Inj. Control Safe. Promot. 2021; ePub(ePub): ePub.

Copyright

(Copyright © 2021, Informa - Taylor and Francis Group)

DOI

10.1080/17457300.2021.2007134

PMID

34852726

Abstract

This study uses structured literature mapping to review worldwide trends in traffic safety following the phenomenon of the COVID-19 pandemic. Motivated by dissimilar findings globally and a lack of evidence from emerging nations which have been significantly more affected by road traffic crashes, the study examines the impact of the pandemic-induced lockdown on road traffic deaths and injuries in Tamil Nadu, India. Using a holistic approach, methods such as ARIMA, Holt-Winters, Bayesian Structural Time Series, and Generalized Additive Model are employed for counterfactual prediction, to draw a causal inference of lockdown on traffic safety. In line with global studies, a substantial reduction in traffic crashes, injuries, and fatalities during lockdowns has been found. However, the comparison of relative differences shows that the number of grievous injuries reduced more than minor injuries, crashes, or fatalities. Furthermore, these relative differences were sustained even when metrics returned to normalcy in the post-lockdown phases. Further spatial stratification at two levels (cities and districts) shows that the macroscopic state-level trends are also broadly seen in the sub-units. This validates the consistency of trends across rural-urban differences and shows that, despite variations in the degree of enforcement of the lockdown within Chennai city, contrary to expectation, increased police presence did not have a differential impact on road crashes.

Keywords: CoViD-19-Road-Traffic

Language: en

Keywords

traffic safety; Bayesian inference; Pandemic; counterfactual prediction; lockdown; road crashes