Modelling passengers, buses and stops in traffic microsimulation: review and extensions
Bibliographic record
Abstract
Abstract In the last decade, significant research efforts and technology have been dedicated to the development of microsimulation tools for a better representation of traffic systems. As a result, several commercial packages appeared and they are used nowadays in the detailed modelling of different transportation systems and operations for specific project evaluations and local designs, mostly within the urban context. After reviewing the specialized literature, we realized that most of these microsimulation tools are oriented to the movement of cars, leaving the public transportation systems as a complement, just for a realistic representation of the transportation system as a whole, but always oriented to simulate cars. In this paper, the objective is to provide guidelines on how to incorporate the necessary entities and components for a proper simulation of public transport systems in a microsimulation environment. Thus, the different approaches to simulate transit systems at a microlevel are discussed, highlighting the necessity of including stops, passengers and transit vehicles explicitly as entities within the microsimulation environment, for modelling transfer operations, control strategies, etc. Several examples are then provided to quantify the impact of such representations, for different cases and potential simulation platforms. Copyright © 2010 John Wiley & Sons, Ltd.
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 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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".