Public Perception of Automated Shuttles for the Last-Mile Connectivity in Montreal
Bibliographic record
Abstract
This thesis investigates the public perception and acceptance of automated shuttle services for last-mile connectivity in Montreal. Through a comprehensive survey, the study examines key factors influencing acceptance of the autonomous shuttle, including experience, awareness, comfort and safety level, trust in technology, benefits and barriers, and potential integration into urban transportation systems. A survey of Montreal residents (n=52) reveals key insights into demographic trends and attitudes towards autonomous vehicles (AVs). Results indicate a moderate familiarity with AVs (38.6%) compared to the US (70.90%), UK (66%), and Australia (61%). Despite this, Montrealer’s expressed positive sentiments towards AVs (54%), slightly higher than the UK and US. Concerns about safety (49% very concerned), legal liability (47.10% very concerned), and data privacy (63.50% very concerned) were prominent. Comfort levels with autonomous technology varied, with 38.45% having heard of autonomous shuttles but only 13.46% having boarded one. Respondents showed preference for level 3 automation (56%) over higher levels. Concerns about interactions with other vehicles, pedestrians, and bikers were noted. Overall, Montreal residents are open to AVs but harbor significant concerns, highlighting the need for targeted interventions to address safety, security, and privacy issues in deploying automated shuttle services effectively. \nKeywords: Automated Shuttles, Comprehensive Survey, Urban Transportation, Demographic.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".