Cellular Automaton Simulation of Vehicle Dynamics in Park-and-Ride Facilities
Bibliographic record
Abstract
Many metropolitan areas offer park-and-ride facilities to help increase transit ridership and reduce congestion. However, studies of vehicle movements within such facilities are few. A thorough understanding of the vehicle dynamics will benefit both transportation planners in forecasting demand for park-and-ride and facility designers in making objective evaluation of alternative layouts. This paper presents a microscopic park-and-ride simulation model using the Cellular Automata approach. Review of parking lot simulation models in the past are performed, and their limitations are identified and addressed in the paper. Different techniques of CA applications in traffic and pedestrian modeling are analyzed and applied in the model. The park-and-ride model is a discrete time and space model composed of five fundamental components that operate in each time step. These components together simulate a variety of driver actions such as surveying of environment, making parking choice decisions, steering and controlling of vehicles. With Kipling Station South lot in Toronto as the testing site, the AM Peak period traffic is simulated. Using vehicle arrival rate and gate processing time as model input, the model is able to generate a realistic simulation of vehicle dynamics within the facility. The parking occupation trend is comparable with the observed trend. The use of simulation in comparing two different parking lot designs is also performed to illustrate one of the many uses of the model.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Cellular-automaton simulation of vehicle dynamics in park-and-ride lots; a transportation-engineering question.
This models vehicle movement in park-and-ride facilities, not research practice.
Transportation simulation of park-and-ride vehicle dynamics; traffic engineering, not research.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".