Investigating Factors Contributing to Urban Traffic Incident Risk Using High‐Resolution Heterogeneous Data
Bibliographic record
Abstract
Urban traffic incidents are among the leading causes of death, injury, and traffic congestion in metropolises worldwide. This paper aims to investigate the effects of social demography, road networks, land use, and public transportation facilities on the traffic incident risk in Shenzhen, China. High‐resolution heterogeneous data are collected for 4207 grids which are used as the basic geographic units. The traffic incident risks of grids are divided into four levels according to the number of incidents. A generalized ordered logit (GOL) model is developed to explore the contributions and elasticity of the variables. The results show that the effect of the significant variables on various thresholds is different. Employment density has a more significant impact on the risk level than population density. Compared to nonworking days, trips during working days are more relevant to the traffic incident risk. Road network variables such as the length of various road types and intersections are positively correlated with the incident risk. For the land use variables, most of them are significant influencing variables. The findings of the paper can provide some suggestions for safety management policies of arterial intersections, critical infrastructures, and future urban land use planning.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".