IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i3p2675-d1054936.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/3/2675/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/3/2675/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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).
    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. 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).
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2020. "Forecasting realized oil-price volatility: The role of financial stress and asymmetric loss," Journal of International Money and Finance, Elsevier, vol. 104(C).
    2. Nahapetyan Yervand, 2019. "The benefits of the Velvet Revolution in Armenia: Estimation of the short-term economic gains using deep neural networks," Central European Economic Journal, Sciendo, vol. 6(53), pages 286-303, January.
    3. Carlos Medel, 2017. "Forecasting Chilean inflation with the hybrid new keynesian Phillips curve: globalisation, combination, and accuracy," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 20(3), pages 004-050, December.
    4. Dombi, József & Jónás, Tamás & Tóth, Zsuzsanna Eszter, 2018. "Modeling and long-term forecasting demand in spare parts logistics businesses," International Journal of Production Economics, Elsevier, vol. 201(C), pages 1-17.
    5. Amita Gajewar & Gagan Bansal, 2016. "Revenue Forecasting for Enterprise Products," Papers 1701.06624, arXiv.org.
    6. Tao XIONG & LI Chongguang & Yukun BAO, 2017. "An improved EEMD-based hybrid approach for the short-term forecasting of hog price in China," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 63(3), pages 136-148.
    7. Pieter van der Spek & Chris Verhoef, 2014. "Balancing Time‐to‐Market and Quality in Embedded Systems," Systems Engineering, John Wiley & Sons, vol. 17(2), pages 166-192, June.
    8. Hyndman, Rob J. & Ahmed, Roman A. & Athanasopoulos, George & Shang, Han Lin, 2011. "Optimal combination forecasts for hierarchical time series," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2579-2589, September.
    9. Kourentzes, Nikolaos & Petropoulos, Fotios & Trapero, Juan R., 2014. "Improving forecasting by estimating time series structural components across multiple frequencies," International Journal of Forecasting, Elsevier, vol. 30(2), pages 291-302.
    10. Tendai Makoni & Delson Chikobvu, 2023. "Assessing and Forecasting the Long-Term Impact of the Global Financial Crisis on Manufacturing Sales in South Africa," Economies, MDPI, vol. 11(6), pages 1-17, May.
    11. Fijorek Kamil & Leśniewska Agnieszka, 2012. "Statistical Forecasting of the Indicators of Polish Airport’s Operations," Folia Oeconomica Stetinensia, Sciendo, vol. 11(1), pages 7-7, January.
    12. Alysha M De Livera, 2010. "Automatic forecasting with a modified exponential smoothing state space framework," Monash Econometrics and Business Statistics Working Papers 10/10, Monash University, Department of Econometrics and Business Statistics.
    13. Spiliotis, Evangelos & Makridakis, Spyros & Kaltsounis, Anastasios & Assimakopoulos, Vassilios, 2021. "Product sales probabilistic forecasting: An empirical evaluation using the M5 competition data," International Journal of Production Economics, Elsevier, vol. 240(C).
    14. Miroslav Navratil & Andrea Kolkova, 2019. "Decomposition and Forecasting Time Series in the Business Economy Using Prophet Forecasting Model," Central European Business Review, Prague University of Economics and Business, vol. 2019(4), pages 26-39.
    15. G'abor Petneh'azi & J'ozsef G'all, 2019. "Mortality rate forecasting: can recurrent neural networks beat the Lee-Carter model?," Papers 1909.05501, arXiv.org, revised Oct 2019.
    16. Thierry Moudiki & Frédéric Planchet & Areski Cousin, 2018. "Multiple Time Series Forecasting Using Quasi-Randomized Functional Link Neural Networks," Risks, MDPI, vol. 6(1), pages 1-20, March.
    17. Carlos A. Medel, 2015. "Probabilidad Clásica de Sobreajuste con Criterios de Información: Estimaciones con Series Macroeconómicas Chilenas," Revista de Analisis Economico – Economic Analysis Review, Universidad Alberto Hurtado/School of Economics and Business, vol. 30(1), pages 57-72, Abril.
    18. Manuela Royer-Carenzi & Hossein Hassani, 2025. "Deviations from Normality in Autocorrelation Functions and Their Implications for MA(q) Modeling," Stats, MDPI, vol. 8(1), pages 1-37, February.
    19. Svitlana Galeshchuk, 2017. "Technological bias at the exchange rate market," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 24(2-3), pages 80-86, April.
    20. Meira, Erick & Cyrino Oliveira, Fernando Luiz & de Menezes, Lilian M., 2022. "Forecasting natural gas consumption using Bagging and modified regularization techniques," Energy Economics, Elsevier, vol. 106(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:2675-:d:1054936. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.