The Who and Where of Road Safety: Extracting Surrogate Indicators from Smartphone-Collected GPS Data in Urban Environments
Bibliographic record
Abstract
Environment and driver behavior are significant contributory factors in traffic collisions. Surrogate safety measures, non-crash measures that are physically and predictably related to crashes, provide opportunities for user-centric approaches to road safety and reduce dependency on crash data in location-centric approaches. The purpose of this study is to extract surrogate safety measures from the smartphone-collected GPS data of regular drivers and to analyze those measures from a location-centric and user-centric perspective. GPS travel data was collected using the Mon Trajet smartphone application in Quebec City, Canada over 21 days. Crash data was obtained from the Ministry of Transportation Quebec for a five year period from 2006 to 2010. The selected surrogate indicator, hard braking events (HBEs), demonstrated a spatial correlation of 0.67 with collision occurrence. Despite strong correlation, HBEs tend to overestimate risk on highway facilities and underestimate risk on local and arterial streets as the sample data collected from regular drivers likely over-represents travel on highways and under-represents travel on urban streets. The user-centric analysis showed that more HBEs occur during the AM and PM peak periods, and that braking in the PM peak period tends to be more severe, demonstrating that HBEs are not only spatially correlated with actual collision occurrence, but also make sense intuitively with respect to the behaviors related to collision occurrence. Future work will determine if other surrogate indicators that are more closely correlated with collision occurrence can be extracted, and disaggregating the analyses by facility type should improve the results.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| 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) | 0.000 | 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".