Application of Dynamic Traffic Assignment (DTA) Model to Evaluate Network Traffic Impact During Bridge Closure - A Case Study in Edmonton, Alberta
Bibliographic record
Abstract
The application of macroscopic travel demand models to quantify traffic operational performance measures, such as delay, queues, level of service, and corridor travel time has some significant limitations. Due to the lack of temporal variation of traffic flow in Static Traffic Assignment (STA) and allowance of demand over capacity in macroscopic travel demand models, the validity and reliability of traffic diversion estimate from major road/bridge closures are often subject to question. Dynamic Traffic Assignment (DTA), on the other hand, is a new and evolving technique which is sensitive to time dependent congestion phenomenon and thus can properly estimate traffic diversion to alternate routes during temporal/spatial traffic flow shifts induced by network supply or traffic demand changes. In summer 2013, the City of Edmonton closed the Stony Plain Road Bridge crossing over Groat Road for four months as part of its roadway rehabilitation program. In order to estimate traffic diversion and evaluate network traffic impacts during the construction period, a DTA model was developed using the Dynameq program. Unlike most models where both the calibration and validation data is collected from the same traffic condition, this model utilized the bridge open (pre-construction) traffic data for model calibration, and the bridge closure (during-construction) data for model validation. Additionally, since traffic demand before and during the short-term bridge closure will likely be the same, the assessment of the model forecasting capability can be considered more credible. This paper presents the DTA model development and traffic impact evaluation process, which covers data collection and analysis, traffic origin-destination demand adjustment, the DTA model network preparation, as well as model calibration and validation using the traffic conditions observed before and during the Stony Plain Road Bridge closure. It is expected that the findings and lessons learned from this study will provide practitioners the understandings and benefits of a DTA model in the application of traffic operational analysis. Recommendations on how to apply a calibrated DTA model to a short-term network supply change are also highlighted.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".