Dynamic Calibration of a Carsharing System in Multiagent Transport Simulation
Bibliographic record
Abstract
When employing simulation platforms to reproduce user behaviors of a transport mode as close to reality as possible, the model used must be calibrated. With simulation software, several calibration methods can be used. Some models are calibrated manually: users modify the parameters to be calibrated, launch the simulation, and then evaluate the results. Users perform these steps until the simulation results satisfy the desired objective. In this study, the multiagent transport simulation (MATSim) platform was used to simulate user behavior across various transportation modes. In addition to known transportation modes (private cars, public transit, and walking), carsharing mode was added to the Siouxfalls scenario. This enhanced scenario was used as the baseline to simulate the impact of the chosen parameters on the agent’s behavior. Previous studies on the calibration of agent-based models such as MATSim have introduced two automated methods based on entropy and gradient descent. However, these methods can require a higher number of calibration iterations and longer simulations to reach the required parameters. To offer both an efficient method in relation to computation time, and a solution that is adapted specifically to the carsharing mode, this paper presents a calibration methodology based on a surrogate algorithm that will act as a substitute for MATSim during calibration to speed up the process. Assuming that the parameters related to the basic mode of transportation were calibrated, the process described in this paper was applied specifically to the calibration of the carsharing mode in MATSim.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".