Concurrent Estimation of Origin-Destination Flows and Calibration of Microscopic Traffic Simulation Parameters in a High-Performance Computing Cluster
Bibliographic record
Abstract
This paper is aimed at developing an optimization framework for the concurrent calibration of demand and supply parameters in a dynamic traffic assignment (DTA) model. The proposed approach calibrates route choice, along with drivers’ behavioral parameters, and estimates origin-destination (OD) flows in a large-scale network in a Paramics microscopic traffic simulation model. A mathematical formulation is defined to quantify the reliability of the observations. A genetic algorithm (GA) is selected as a suitable solution algorithm for the resulting nonlinear stochastic optimization problem. The application of the proposed methodology is implemented in the large-scale network in the business district core of downtown Toronto, Ontario, Canada. For this network, the emerging traffic surveillance data from in-vehicle navigation system technology provide an enriched source of disaggregated speed data. The empirical results from various experiments support the hypothesis that incorporating in-vehicle navigation system speed data can improve the calibration accuracy and minimize the reliance of the calibration process on a priori OD flows. The quality of the solution and convergence speed of a GA is further enhanced by dividing the GA population into multiple demes and running the GA on a high-performance computing cluster (HPCC) with multiple processors (i.e., parallel distributed GA, PDGA). In addition, this research takes a further step toward analyzing the temporal variations of the driving behavior of travelers. The case study establishes an example for modelers and practitioners who are interested in calibrating a large-scale traffic simulation model. The developed simulation model for traffic has the potential to serve as a test bed on a HPCC for more efficient computation and integration with other optimization tools such as GAs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".