Understanding effect of traffic and driver related characteristics on seat belt usage in Mumbai city using random parameter logit approach and time series analysis
Bibliographic record
Abstract
Safety seat belt usage has been a great interest to the transportation community. Understanding factors that influence driver's decision of wearing a safety seat belt or not is essential in determining ways to enhance safety seat belt usage rate. A modeling approach is made to observe the trend of seat belt usage in Mumbai city and to understand the effect of vehicle type, ownership type, driver's sociodemographic, and environmental characteristics on safety seat belt usage in Mumbai City. Data were collected by roadside observational surveys at various locations in Mumbai during the years 2015 through 2018. The time series model estimate confirms declining trend of drivers not wearing safety seat belt. When vehicles are disaggregated into different build types, buses are found to be associated with no use of safety seat belt as compared to other type of vehicles, and even male drivers follow the same trend in the city. By using random parameter logit model unobserved heterogeneity was captured among individuals. Findings can be used by policymakers to develop intervention strategies to increase seat belt usage in Mumbai and other cities having similar traffic characteristics and social environment features.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".