Scalable Transportation Monitoring using the Smartphone Road Monitoring (SRoM) System
Bibliographic record
Abstract
Our quest for ubiquitous Intelligent Transportation Systems (ITS) is simply infeasible over proprietary systems. In a time of abundant smart devices, it is impractical to consider developing competing proprietary monitoring systems to collect information for ITS operation. We argue for utilizing smartphones to present a driver and road monitoring system capable of scaling to the number of drivers without incurring high implementation costs, thus allowing for safer driving conditions and shorter accident response times. We propose the Smartphone Road Monitoring (SRoM) system that is capable of sensing road artifacts such as potholes and slippery roads. The information is collected through crowdsourcing and processed by base stations, giving faster and more accurate responses compared to current systems, to address road safety related events in a timely manner. It is also capable of detecting aberrant driver behavior such as speeding and drifting. SRoM uses both the driver's smartphone and vehicle as sources of information, and allows pedestrians to share media pertaining to each event. The collected data is made available to the public through an interactive map updated with the authenticated events. We implemented a prototype of the system to perform the task of safety monitoring. System evaluation of the prototype shows that the system can be easily implemented in real-life using current technologies at little cost.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".