Challenges in the prediction of motor vehicle traffic collisions with GPS travel data
Bibliographic record
Abstract
In the field of road safety, crashes involving physical injuries typically occur on roadways, which constrain the events to lie along a linear network. Substantial research efforts have been devoted to the development of methods for point patterns on linear networks. In one such model, we assume that crash coordinates are produced by a Poisson point process whose domain corresponds to edges in the road network. This talk focuses on the analysis of geo-localised accident data in the context of a smart city initiative launched by the City of Quebec (Canada) aiming to identify crash hotspots on the road network based on covariates derived from GPS data. Data originate from three sources: i) a geolocalised traffic accident database whose entries are based on police reports, ii) GPS trajectories obtained from a study on 4,000 drivers involving 55,000 trips and iii) the structure of the road network obtained from the OpenStreetMap (OSM) database. We highlight challenges, both methodological and computational, with the use of those three data sources in producing sensible inference for the covariate effects.
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 | 0.025 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".