Private Car Ownership in Presence of Shared Autonomous Vehicles, Case of Tehran
Bibliographic record
Abstract
With the advent of autonomous vehicles (AVs), researchers have conducted various studies on the impacts of these vehicles, but limited research is found on the influence of AVs on private car ownership. Since AVs are not well-aligned with sustainable development, shared autonomous vehicles (SAVs) would be an appealing alternative. Hence, this paper aims to investigate the impact of socio-economic and travel-related characteristics on private car ownership in the presence of SAVs among private car users in Tehran. After designing a web-based stated preference (SP) questionnaire and analyzing 2154 valid SP responses in 2022, more than a quarter (26%) of the observations are willing to reduce private car ownership level in presence of SAVs. Estimation results of binary logit model reveal that respondents aged 31-35 and 22-25 years old, as compared to other age categories, are more and less, respectively likely to sell their private cars. Further, due to the fact that users of SAVs, unlike private cars, do not need parking space, respondent are more likely to sell their private cars under this condition. Estimated coefficients of attributes considered in the SP scenarios indicate that increasing each of these attributes (travel time, waiting time, travel cost and number of passengers) in SAVs reduce the likelihood of selling a private car. Another important finding is the impact of respondents' experience with internet taxis (taxis ordered via an app on smartphones); those with frequent use or satisfy with this service, are more likely to reduce their car ownership.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".