Autonomous vehicle parking policies: A case study of the City of Toronto
Bibliographic record
Abstract
Autonomous Vehicles (AVs) can eliminate the burden of finding a parking spot upon arrival to the destination. AVs can park at a strategic location or cruise until summoned by their users. In this study, we investigate AV users’ parking decision considering their cost and time constraints. Each users’ decision has impacts on congestion which can change feasible options of other users. Hence, we use an agent-based simulation model to study AV parking policies. Results show that travelers consider sending their vehicles to park at home if they have to pay to use a parking facility. Also, our analysis for downtown Toronto shows that AVs would travel on average 12 min and a maximum of 47 min to park in cheaper parking lots. We also find that assigning the same parking price across all the parking facilities would exacerbate the congestion by motiving more AVs to cruise instead of choosing the closest parking lot. However, we show that a toll for zero-occupant AVs leads to a tradeoff between parking cost and distance that would decrease the VKT by 3.5% in downtown Toronto.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".