A Comparison between Parametric and Nonparametric Approaches for Road Safety Analysis – A Case Study of Winter Road Safety
Bibliographic record
Abstract
In road safety research, a parametric approach is commonly applied in modeling road collisions, which have resulted in many different types of models such as Poisson, Negative Binomial and Poisson lognormal. While easy to apply and interpret, a parametric approach has several critical limitations due to the modeling requirement of assuming a specific probability distribution form for each model variable (e.g. collision frequency) and a pre-specified functional relationship between each model parameter and the predictors. These assumptions, if violated, could lead to biased and/or erroneous inferences on the effect of these predictors on the dependent variable. This paper introduces a data-driven, nonparametric alternative called Kernel regression, which circumvents the need for the aforementioned assumptions. This paper compares the parametric and nonparametric approaches through an empirical study using a large dataset consisting of hourly observations of collisions, road weather and surface conditions, and traffic counts from highways in Ontario, Canada, over six winter seasons. It is shown that the nonparametric approach has the advantage of being able to capture the significant nonlinear and interacting effects of some condition factors. The paper also illustrate the practical implications of the differences between the two approaches, including evaluation of the risk levels of road surface conditions for the road users and quantification of safety benefits of maintenance operations for transportation authorities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.036 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".