Modeling Network Capacity for Urban Multimodal Transportation Applications
Bibliographic record
Abstract
Since the diversity of urban transport modes and the growth of public transport demands recently, it is essential to consider the multiple mode options in the network capacity problem. This paper derives a comprehensive network capacity model from a single-mode transportation network with only route choice to a multimodal transportation network with both mode choice and route choice. To avoid biases in the evaluation of the multimodal network capacity, two characteristics of the multimodal transportation system are considered in modeling and formulating the problem: (1) the mode interaction between cars and buses is explicitly reflected when they share the same link; (2) the correlation of travel alternatives (modes or routes) is measured by developing a combined modal split and traffic assignment (CMSTA) problem, in which the nested logit (NL) model is employed to account for mode similarity in mode split, while the path-size logit model (PSL) is employed to account for route overlapping in traffic assignment. Numerical experiments demonstrate the characteristics of the new model. It also shows how planning schemes or management strategies affect the multimodal transportation network capacity via a real network case.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".