Estimation of Safety Performance Measures from Smartphone Sensors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Safety performance measures represent an useful tool for evaluating road safety conditions on the basis of objective parameters deducible from the vehicle kinematics. In this context, safety performances are expressed in terms of indicators representing interactions between different pairs of vehicles belonging to the traffic stream. Safety performance is expressed from the perspective of rear-end vehicle interactions. Differences in safety performance are discussed with respect to type of indicator and traffic conditions. When these indicators reach a certain critical value (threshold), a possible accident scenario is identified. Most common approaches used to acquire vehicle tracking data are based on video image processing algorithms and satellite navigation systems. However, many studies are increasingly interested in the emerging smartphone technologies for tracking people, and hence vehicles. Due to the fact that smartphones are becoming a valid alternative to Tablets, PDAs and laptops, offering phone features coupled with multiple mobile internet applications, smartphone sales will more than triple to 491.9 million units by 2012 from 139.3 million in 2008 (Gartner Inc. forecasts). The main goal of this study is to present a procedure for extracting vehicle tracking data from smartphone sensors and to use them in the estimation of safety performance indicators. The accuracy of tracking data from smartphone sensors is evaluated with respect to GPS tracking measurements. The results of this analysis identify interactions potentially dangerous and highlight high risk zones that reflect locations characterized by high vehicular interactions. This study underscores the usefulness of the smartphones for providing meaningful experimental data to assess potential safety problems.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it