Integrated Passenger and Commercial Vehicle Model for Assessing the Benefits of Dedicated Truck-Only Lanes on the Freeways
Bibliographic record
Abstract
The concept of dedicated truck-only lanes has been proposed more than twenty years ago and since then, several jurisdictions have undertaken studies exploring the concept. While the nature of these studies range from research, to proof-of-concept to detailed design-and-build project studies, most of these studies have a common issue to deal with, i.e., predicting/forecasting the commercial vehicle demand, along with all the other class of passenger vehicles demand as well as system-wide impacts (both benefits and disbenefits) of various truck-only treatments for evaluation purpose. This is the primary focus area of this paper. Travel demand forecasting models, either regional or state/province-wide, are a key planning tool for such studies. Two key areas related to demand modelling of commercial vehicle demand are a) how well it is integrated with the passenger demand models, and b) how the parameters related to truck traffic behavioural aspects have been developed and incorporated. This paper will include a cursory review of some of the previous studies with regard to demand modelling approach and methodology, in particular the two issues mentioned, as well as evaluation techniques. The second part of the paper presents an overview of a case study involving a strategic assessment of truck-only lanes in the freeway network in a regional context within the Greater Golden Horseshoe, in Central Ontario. The use of a regional macro-level travel demand model for the strategic analysis and a mesoscopic sub-area model will be presented, along with a discussion of technical results of these two analyses. For the covering abstract of this conference see ITRD record number 201211RT334E.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".