Conflict-Based Safety Performance Functions for Predicting Traffic Collisions by Type
Bibliographic record
Abstract
In road safety analysis, safety performance functions (SPFs) are used to predict the average number of collisions per year at a road site. SPFs are a function of various amounts of exposure and, in some cases, site-specific characteristics. Exposure is a measure of opportunities for collisions to occur. The circulating traffic volume is commonly adopted for this purpose. However, not all vehicles interact unsafely at a road site. Alternatively, traffic conflicts may provide a more appropriate exposure measure for collisions because they represent only unsafe interactions between vehicles. There has been some research on this topic, mainly based on aggregated data including all conflict types. In this study a stratified analysis was conducted by type of conflict. Hence, the main objectives of this research were to establish a relationship between predicted collisions and predicted conflicts by using an SPF with traffic conflicts as an exposure measure and to predict the number of specific types of conflicts and collisions at signalized intersections. The methodological framework used was a two-phase nested modeling process in which a Poisson–gamma SPF that uses traffic volume as exposure was used to predict conflicts, which were then used in another Poisson–gamma SPF to predict collisions. The proposed approach was applied to a data set of collision frequency and average hourly conflicts for 49 signalized intersections throughout British Columbia, Canada. The results demonstrate the proportional relationship between conflicts and collisions and the importance of carrying out stratified analyses when types of conflicts are combined.
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 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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".