Evaluating and forecasting the operational performance of road intersections
Bibliographic record
Abstract
Road intersections represent one of the most complex configurations encountered when traversing road networks. It is therefore of vital importance to improve their operational performance, as that can significantly contribute towards the efficiency of the whole transport network. Traditional approaches to improve the efficiency of intersections are based on analysis of static data or expert opinions. However, due to the advancements on Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication technologies, it is possible to enhance safety and improve road intersection efficiency by continuously monitoring traffic conditions and enabling situational awareness of vehicle drivers. Towards this end, we design, develop and evaluate a system for evaluating and forecasting the operational performance of road intersections by mining streams of V2I data. Our system makes use of graph mining and trajectory data mining methods to continuously evaluate a set of well-defined measures of effectiveness (MOEs) for traffic operations at different levels of road network abstraction. In addition, the system enables interactive analysis and exploration of the various MOEs. The system architecture and methods are general and can be used in various settings requiring continuous monitoring and/or forecasting of the road network state.
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 | 0.001 | 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 | 0.001 |
| 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 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".