Impacts of COVID-19 Pandemic Lockdown on Road Safety in Bangladesh
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".