Dynamic, multi- and intermodal bike sharing in agent-based modelling
Bibliographic record
Abstract
Negative effects of traffic, like congestion, air and noise pollution are among the reasons why environmentally friendly solutions are promoted. Bike sharing (bs) is intended to strengthen cycling and public transport. Nevertheless, current transport models rarely consider cycling or even bs, in either detail or holistically. In this paper we present an agent based approach to model cycling and in particular bs within the multimodal simulation environment MATSim. Multimodal trips combining public transport and bs are included as well as within day rescheduling of bs trips as agents may not find a bike or empty return space (parking spot). To minimize such cases, choice probabilities were implemented, so that agents only start their bs trip, if sufficient bikes or parking spots are available. The modules presented in this paper were applied using a MATSim model of the city of Vienna. Agent based bs modelling is an inexpensive option to test the impact of a bike sharing system before implementation.
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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 | 0.000 | 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".