Empirical Investigations of Queuing and Surface Street Times using Truck Probe Data around International Border Crossings
Bibliographic record
Abstract
Truck queuing times and surface street times in the vicinity of two major US-Canada international border crossings were investigated as a function of explanatory variables.Queuing times before primary customs inspection at the Ambassador Bridge and the Blue Water Bridge border crossing facilities were examined as a function of inspection times and truck traffic volumes.Surface street times approaching and departing the Ambassador Bridge facility in Canada were examined as a function of truck traffic volumes.The ability to conduct empirical investigations was possible due to the recent availability of detailed, disaggregate truck trip data that could be used to approximate queuing times, surface street congestion times, and times spent in customs inspection.Appropriate disaggregate volume data were not available, and aggregate volume data were used in the analyses.To control for the use of the aggregate volume data with disaggregate truck trip data, subsets of the data were selected that corresponded to "worst" periods of queuing and surface street times based on time-of-day and day-ofweek patterns.Contingency tables and regression analyses were used to investigate the associations between queuing or surface street times and the explanatory variables.Queuing and surface street model specifications were motivated to be consistent with multiplicative formulations.These formulations were transformed to be linear-in-the-for his dedication and willingness to give his time so generously in support of this research project.I would also like to thank Dr. Mark R. McCord and Dr. Rabi G. Mishalani for the opportunity to work as their Graduate Research Associate -the knowledge and experiences they have shared with me have been invaluable to my growth and will be immensely beneficial in my future endeavors.I would be remiss in not directly thanking Dr. Frank Croft, whom without his
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".