Bibliographic record
Abstract
Land border crossings in North America that serve high volumes of automobile and truck traffic experience congestion and delay, resulting in adverse effects on level of service, transportation costs, commerce, tourism, and the environment. Although efforts have been underway to expedite the customs inspection process (without compromising security), improving the reliability of delay estimates and real time dissemination of information remains a challenge. This paper reports research on intelligent technologies and methodological advances that can be used to automatically predict private and commercial vehicle queues and delays and display information to motorists, border crossing authorities, and other decision makers on a real time basis. The availability of such traveler information can be useful for making pre-trip and enroute travel decisions by private motorists as well as commercial vehicle operators regarding departure time and choice of border crossing location (if applicable). Such a system would enable motorists and carriers to avoid severe delays and commercial vehicle fleet efficiency gains can be achieved. Border crossing authorities can use the results to better match the processing capacity with demand for service. Research steps include the use of a calibrated microsimulation model of the Windsor-Detroit Ambassador Bridge crossing, development of artificial neural network (ANN) models for predicting queues and delay, imbedding these models in a traveler information system that uses sensor data as input and produces delay predictions for dissemination on dynamic message signs and other media on a real time basis. This system is tailored for border crossings with high volumes of private and commercial vehicle traffic.
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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".