The application of latent class analysis for investigating influential factors for crashes involved cyclists in Toronto, Canada
Bibliographic record
Abstract
The heterogenous nature of crash data has always been a challenging barrier in interpreting data processing results and in unrevealing the hidden relationships between crash contributing factors. Traffic researchers aim to eliminate the interruption of insignificant factors , identify influential factors and sort out complicated logic relationships among those factors. Understanding the influential factors leading to traffic accidents is essential for designing effective countermeasures. A method commonly employed to address systematic heterogeneity in crash data is to focus on each subgroup of data. However, this approach neglects the independent relationships among factors, and does not ensure homogeneity within each subgroup. In this project, Latent Class Cluster analysis is applied to segment a whole cyclist crash dataset into homogenous subgroups with meaningful influential factors. The manuscript employs data from recorded crashes involving cyclists from 2008 to 2018 by the police in Toronto, Canada. The analyses demonstrate that dividing cyclists’ crash data into seven clusters most efficiently helps in reducing the systematic heterogeneity of the data and aids in understanding the relationships between socio-demographic characteristics, environmental characteristics, maneuver-related characteristics etc. and further identifying determining reasons for the crashes involving cyclists . Based on the clustering results, some significant factors are studied in detail along with socio-economic backgrounds and some countermeasures are proposed. Overall, this study suggests that a latent class clustering approach is suitable for reducing heterogeneity and revealing important hidden relationships in traffic safety analyses.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".