Application of Dynameq in Montreal: Bridging the Gap between Regional Planning Models and Microsimulation
Bibliographic record
Abstract
This paper describes how the City of Montreal, Canada has implemented a dynamic traffic assignment (DTA) model of the Montreal central business district (CBD) and the 9-km Notre-Dame Street corridor, which covers a total area of 100 km2. This DTA model, which was implemented with Dynameq software, bridges the gap between travel demand models for long-range planning of metropolitan regions and microsimulation software for traffic operations. Three different situations were analyzed. The first is the transformation of an existing urban expressway into a signal free access controlled facility. The second is a downtown exit ramp serving the regional transit hub. The third is an urban arterial with various re-design alternatives and its impacts on through traffic in the adjoining neighborhood. In each case, the incompatibilities between the regional planning model and the required inputs for a microsimulation model were resolved by the use of the DTA model as an intermediate step between these two fundamentally different methodologies. The static modeling was carried out with EMME/2. Dynameq was used to generate an equilibrium DTA on a subarea from EMME/2. Traffic signal timing plans were optimized in Synchro using Dynameq outputs, and then fed back into Dynameq, looping several times if required and the results were visualized in Dynameq and SimTraffic. The benefit of this three-level methodology (static assignment, DTA, and micro-simulation) was excellent calibration results at all levels of network resolution with modest resources, as well as consistency among the microsimulation study areas. As data scrutiny for multi-million dollar evaluation studies for redevelopment projects compels analysts to seek unprecedented levels of data confidence, using a DTA model to link the planning and microsimulation paradigms has become a necessity.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".