Road User Collision Prediction Using Motion Patterns Applied to Surrogate Safety Analysis
Bibliographic record
Abstract
Surrogate safety analysis is the process of diagnosing road safety indirectly from measures of ordinary (non-collision) road user behaviour, such as absolute speed and time-to-collision. While absolute speed has enjoyed much popularity in the literature, other measures such as time-to-collision are currently under developed. Before conflict measures such as time-to-collision can be adopted, several challenges need to be overcome, notably the problem of accurately modeling collision courses and collision probability from normal road user behaviour. This paper describes and explores the feasibility of implementing discretized motion pattern maps for the purpose of predicting potential collisions between road users and their measures based on an empirical naturalistic behaviour model calibrated from site-specific data for use in surrogate safety analysis. The methodology is applied to a pre-existing framework which extracts road user trajectory data from video data of a traffic scene, and then predicts and estimates potential collisions. To this end, this paper examines the motion pattern model discretization process, the probabilistic framework, the resulting indicators, and then compares the motion prediction methodology with that of the classical constant velocity motion prediction methodology. The methodology is explored using road user behaviour inside the weaving zone of a roundabout to illustrate the flawed use of constant velocity motion prediction.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".