Investigating of machine learning's capability in enhancing traffic simulation models
Bibliographic record
Abstract
ABSTRACT: The development of agent-based modeling in traffic simulation allows for the modeling of traveler movement and decision making using predefined rules and variables. Nonetheless, the computational cost of agent-based modeling is high, and it takes a long time to generate new scenarios using these models. To address this, this study proposes a new approach to predict the results of new simulations using machine learning techniques. This paper focuses on the reproduction of the models that simulate variables reflecting traveler decision making, such as mode choice, travel distance and duration, and waiting time. A variety of data-driven techniques have been employed in this regard to model these features resulting from unanticipated activities in a dynamic environment. The proposed approach will be based on synthetic data generated from various simulation scenarios, that will be followed by a data preparation process. Therefore, the robustness of the built machine learning models was tested and assessed in different and new situations in order to evaluate their capability to reproduce the models responsible for generating the stated variables. Experiments show that the suggested solution has a high level of robustness, implying that it can replicate the final results of these models. Further, Extreme Gradient Boosting outperformed other machine learning techniques in terms of predicting simulation variables when comparing prediction accuracy and running time.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".