Exploring Agent-Based Modelling for Car-Based Volunteer Driver Program Planning
Bibliographic record
Abstract
Volunteer driver programs (VDPs) utilize the service of volunteers to replicate car-based, demand-responsive, door-to-door services in rural areas, but little is understood about how external factors (e.g., changes in service area) affect VDP sustainability. Agent-based modelling (ABM) simulates the operational behavior of individual agents (e.g., drivers, users) to evaluate their interaction under specified scenarios, and although it has been used in transportation research, it has never been applied to VDP analysis. Netlogo was used to develop a simplified VDP ABM, calibrated and validated with 1 year of program data from the New Brunswick Volunteer Driving Database. Three model scenarios were tested: increased health trip distance, increased service area, and increasing the number of drivers to meet initial distance targets. The ABM demonstrated intuitive results and established connections among changing operational scenarios, though additional research is needed for multipurpose trips and user/driver/dispatcher interactions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".