Who’s Driving Change? Potential to Commute Further using Automated Vehicles among Existing Drivers in Southern Ontario, Canada
Bibliographic record
Abstract
Although automated vehicles (AVs) are rapidly being developed, the public sector continues to learn what this technology could mean for transportation policy. As reducing auto-based commute distances is one common planning objective, understanding the conditions under which individuals may adopt AVs to commute further is important. To that end, this study uses data from a 2016 survey of residents in Southern Ontario, Canada, to estimate the characteristics and motivations of individuals indicating the most interest in commuting further using AVs. Comparing findings with existing research on commute lengths and AV adoption, this study identifies how AV adoption may shape long-distance commuting, focusing on commuters who already drive to work. Results suggest significant potential for longer commutes and that longer AV-based commutes are expected to be taken by younger, higher educated, and tech-savvy individuals who already travel by car for regular daily trips, and by older individuals residing in urban neighborhoods. Findings on longer commutes, gender, and domestic responsibilities suggest that longer commutes could reinforce existing disparities in commuting distances between men and women but may be valuable to individuals with occasional chauffeuring responsibilities—suggesting potential for broader impacts in household social roles. Those expected benefits from AVs anticipated to motivate longer commutes include multitasking, safety improvements, better reliability, improved parking, and reduced traffic—suggesting that should AV technologies deliver in these realms, select commuters may derive significant utility. Paradoxically, several of the benefits expected to deliver the most consumer value may be undermined should additional AVs increase traffic significantly.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 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".