Real-Time Evaluation Method of Vehicle Conflict Risk on Urban Expressway Based on Smartphone GPS Data
Bibliographic record
Abstract
To effectively evaluate the traffic safety risk of urban expressways in real time and ensure their traffic safety and smoothness, a real-time evaluation method of vehicle conflict risk of an urban expressway based on smartphone GPS data was proposed. We screened and processed smartphone GPS data to obtain vehicle behavior data, including acceleration and angular acceleration, and road state data, including average vehicle speed. Urban expressways were divided into four categories, closed straight section, closed curve section, vehicle entry section, and vehicle exit section; the evaluation indexes of abnormal vehicle behavior were established. Based on the improved entropy weight method, the vehicle conflict risk entropy was established to distribute the weight of different types of abnormal behaviors of vehicles. The evaluation system of vehicle conflict risk entropy was applied to the vehicle behavior data. Urban Expressways with more abnormal vehicle behavior were obtained to evaluate the risk of vehicle conflict in real time. The results showed that the easily obtained smartphone GPS data may be effectively used to analyze the abnormal behavior of vehicles, identify vehicle conflict risk points hidden in urban expressways in real time to provide effective methods for batch and dynamic real-time evaluations of vehicle conflict risks on urban expressways, and improve the traffic safety service level of urban expressways.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".