Comparison of MATSim and EMME/2 on Greater Toronto and Hamilton Area Network, Canada
Bibliographic record
Abstract
The agent-based microsimulation modeling technique for transportation planning is rapidly developing, is being applied in practice, and is attracting considerable attention. Along with the conventional four-step modeling technique, MATSim and EMME/2 represent two genres of traffic assignment. They are built on different theoretical bases: dynamic stochastic stationary state assignment and static deterministic user equilibrium assignment, respectively. A study was done of the models' application with data from the Greater Toronto and Hamilton area network in Canada. Given the actual demand data, the models' assignment results are compared and validated on the basis of four indicators of the road network—travel time, travel distance, link volume, and link speed—to reflect both spatial and temporal variation of the traffic flow pattern. The comparison results show that numerical outputs produced by MATSim are not only compatible with those by EMME/2 but are also more realistic from a temporal point of view. The agent-based microsimulation model can be an appropriate alternative to the conventional model for transportation planning. Therefore, agent-based microsimulation models reflect a promising direction of next-generation transportation planning models.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".