Usage Intention of Shared Autonomous Vehicles with Dynamic Ride Sharing on Long-Distance Trips
Bibliographic record
Abstract
Technology advancements have paved the way for public access to shared autonomous vehicles (SAVs), but there is still no travel survey examining how SAVs with dynamic ride sharing (DRS) affect long-distance (LD) trips. Given the growth in these trips and the higher importance of travel time and cost on LD trips, assessing potential impacts of SAVs could be a vital tool in planning for a sustainable transportation system. This paper examines the impact of various attitudinal, sociodemographic, and travel-related characteristics on the usage intention of SAVs with DRS on LD trips. We have designed and conducted a web-based survey for this purpose and based on a representative sample of 723 individuals in 2021, a Generalized Ordered Logit model is estimated. Estimation results highlight the key importance of following psychological factors in a descending order: price evaluation, perceived usefulness, consumer innovativeness, sharing attitude, and privacy concern. Further, key factors among sociodemographic and travel-related characteristics are gender, education level, driving license, household car ownership, generational element, and crash history. These findings provide crucial insights into the likely effects of SAVs with DRS on LD trip behavior, based on which a number of practical implications are proposed for facilitating policy-making.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".