Car ownership, carsharing, neighbourhood types and travel attitudes: A latent-cluster analysis
Bibliographic record
Abstract
• Analyzes car ownership and carsharing decisions using Montreal (Canada) data. • Identifies four types of residential neighbourhoods using latent-cluster analysis. • Identifies four attitude-based profile of respondents using latent-cluster analysis. • Cross-analyzes the two segmentations to investigate car ownership/carsharing choice. • MNL confirms the influence of neighborhood types and attitude profiles on choice. The availability of carsharing in cities around the world has allowed more households to take advantage of the service as an alternative or a complement to private car ownership. While most research has looked at the effect of carsharing on car ownership decisions using carsharing users’ surveys, very few have modelled the choice of car ownership and carsharing jointly using independent surveys. This paper investigates the complex relationship between this joint decision, the built environment, and travel-related attitudes. Using data from two surveys in Montreal, latent-cluster analysis is used to identify a typology of residential neighbourhoods and a segmentation of attitude profiles. Cross-analyzing the two segmentations suggests that those with more positive attitudes towards the car are more likely to own cars and less likely to join carsharing across all neighbourhood types compared to less car-oriented profiles. However, people from all attitude profiles own fewer cars in more central neighbourhoods than in more suburban locations. Finally, a MNL model where sociodemographics and residential parking are controlled for confirms that both the built environment and attitudes independently influence the joint decision. Results also suggest that attitudes are associated with residential location choice, hinting at the presence of residential self-selection or environmental determinism. In summary, the analysis indicates that policy measures aimed at expanding carsharing vehicle availability for promoting carsharing as an alternative to car ownership may primarily impact individuals who are less drawn to the symbolic and emotional aspects of traditional cars.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".