Spatial and Energy footprints of Cars in Cities: New Metrics and Illustrations for the Montreal Area
Bibliographic record
Abstract
Private cars are a key component of personal mobility. They however consume a lot of space when parked or travelling and generate numerous externalities. They are among the most visible mode of transportation both due to their size, which has increased over time, and to the infrastructures on which they rely to park and move. This paper proposes new metrics to consider the space and energy consumption of cars in cities. Road network data is used to evaluate the network coverage. Car trips from travel surveys combined to car fleet datasets are used to evaluate the surface (2D) and volume (3D) footprints of cars over time and across space. The energy footprint of car trips is also evaluated, namely regarding the activity and the duration of the activity they allow to reach. Results show the important transformation of the car fleet and the increasing 2D and 3D footprint they have. They also demonstrate that larger cars reduce parking capacity, increase congestion, and prevent energy savings.
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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 | 0.001 | 0.000 |
| 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 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".