Autonomous vehicle parking policies: A case study of the City of Toronto
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it