Analysis of Spatial Concentrations of Large-Truck Crashes Using Data Mining Methods
Bibliographic record
Abstract
In recent years, the number of studies on crashes involving large-trucks has increased due to its importance to the economy and the higher chance of fatalities. However, none of the previous studies has given attention to the spatial concentrations of large-truck crashes. Moreover, the literature lacks exploration of granular level land use and urban design factors. The current study used the DBSCAN (Density-Based Spatial Clustering of Application with Noise) method to identify the spatial concentrations of crashes involving large-trucks. Additionally, the study explored housing, population, employment, and road network density attributes along with the crash characteristics, roadway attributes, location type, traffic conditions, driver’s action and behavior, and environmental factors. The association rule analysis was employed to discover the contributory factors that lead to no injury, non-severe and severe injuries at the spatial concentrations of crashes involving large-trucks. The findings indicated that the rear-end collisions involving drunk drivers often lead to severe injuries in large-truck crashes. Non-interstate roads, speed limit from 40 to 80 kilometers per hour, high road network density, medium and high population density are frequent conditions of non-severe injuries. Lastly, collisions between large-trucks and fixed objects, sideswipe same direction collisions, snowy roads, clear weather, medium road network and employment density are likely to facilitate no injury crashes involving large-trucks. Road traffic authorities can use these insights to reduce the frequency and severity of crashes involving large-trucks at their spatial concentrations.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".