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Impacts of COVID-19 Pandemic Lockdown on Road Safety in Bangladesh

Author

Listed:
  • Shahrin Islam

    (Department of Civil Engineering, Bangladesh University of Engineering & Technology, Dhaka 1000, Bangladesh
    Department of Civil Engineering, Bangladesh University of Business & Technology, Dhaka 1216, Bangladesh)

  • Armana Sabiha Huq

    (Department of Civil Engineering, Bangladesh University of Engineering & Technology, Dhaka 1000, Bangladesh)

  • Sabah Hossain Iqra

    (Department of Civil Engineering, Bangladesh University of Engineering & Technology, Dhaka 1000, Bangladesh)

  • Raas Sarker Tomal

    (Department of Civil Engineering, Bangladesh University of Engineering & Technology, Dhaka 1000, Bangladesh
    Department of Civil Engineering, Bangladesh University of Business & Technology, Dhaka 1216, Bangladesh)

Abstract

The purpose of this research is to demonstrate the trends of crashes, injuries, and fatalities under the effect of the lockdown and observe the deviation of these trends from the anticipated values that would have been seen without the impact of the lockdown. To that end, data on road collisions, injuries, and fatalities in Bangladesh were compiled over four years (from January 2016 to May 2020) using the dataset from the Accident Research Institute (ARI). The pre-pandemic and lockdown period during the pandemic were included in the selected study period. To compare the observed values of the number of crashes, injuries, and fatalities to the forecasted values, which were meant to show assumed conditions without the emergence of the COVID-19 pandemic, different Autoregressive Integrated Moving Average (ARIMA) time series models were developed for each first-level administrative divisions (Dhaka, Chattogram, Khulna, Barishal, Rajshahi, Sylhet, Rangpur, and Mymensingh). Due to the mobility restrictions, the observed number of collisions, injuries, and deaths remained below the expected values, with a discernible high difference throughout the entire lockdown in Dhaka and Chattogram. In contrast, in the case of other divisions (Khulna, Barishal, Rajshahi, Sylhet, Rangpur, and Mymensingh), it remained under the expected trend for most of the lockdown period but not entirely. The mobility was not eliminated, resulting in a non-zero crash, injury, and fatality records across all divisions. In multiple instances, we observed that actual collision, injury, and fatality rates were higher than expected. Additionally, various divisions exhibited varying patterns of crashes, injuries, and fatalities during stay-at-home orders. Poor performance has been noted in terms of overall road safety during the pandemic era. Given the possibility of future waves of COVID-19 cases and other pandemics, the results of the current study can be used by local authorities and policymakers to improve road safety.

Suggested Citation

  • Shahrin Islam & Armana Sabiha Huq & Sabah Hossain Iqra & Raas Sarker Tomal, 2023. "Impacts of COVID-19 Pandemic Lockdown on Road Safety in Bangladesh," Sustainability, MDPI, vol. 15(3), pages 1-22, February.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:2675-:d:1054936
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    References listed on IDEAS

    as
    1. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    2. Carlos A. Medel & Sergio C. Salgado, 2013. "Does the Bic Estimate and Forecast Better than the Aic?," Revista de Analisis Economico – Economic Analysis Review, Universidad Alberto Hurtado/School of Economics and Business, vol. 28(1), pages 47-64, April.
    3. Barnes, Stephen R. & Beland, Louis-Philippe & Huh, Jason & Kim, Dongwoo, 2020. "The Effect of COVID-19 Lockdown on Mobility and Traffic Accidents: Evidence from Louisiana," GLO Discussion Paper Series 616, Global Labor Organization (GLO).
    4. Sai Chand & Ernest Yee & Abdulmajeed Alsultan & Vinayak V. Dixit, 2021. "A Descriptive Analysis on the Impact of COVID-19 Lockdowns on Road Traffic Incidents in Sydney, Australia," IJERPH, MDPI, vol. 18(21), pages 1-17, November.
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