Will you still drive or are you ready to ride? Exploring readiness to use demand-responsive transport in the City of Vienna
Bibliographic record
Abstract
Demand-responsive transport (DRT) is recognized as a potential solution for mitigating car-dependent mobility in areas with underdeveloped public transport systems. However, whether DRT competes with or complements public transport remains unclear. This study investigates these dynamics using the stated preference method and random utility maximization theory, emphasizing the role of long-term mobility patterns, such as years of car driving, alongside specific attributes of DRT and car modes. The research incorporates stated preference (SP) scenarios reflecting diverse travel needs, including urgent versus flexible trips and requirements for transporting luggage, baby stroller, or small bags. Data was collected in Vienna, where DRT services are implemented, comprising 2934 SP choices from 326 respondents. The analysis involved two stages: discrete choice modeling was first used to assess random and systematic effects on decision-making via the Monte Carlo method . The second stage explored substitution effects, willingness-to-pay, and policy implications for DRT deployment. Findings indicate that only 9.35 % of participants perceive years of car driving as positively influencing their readiness to use DRT, while for 90.65 %, more driving experience negatively affects DRT utility. Parking fee increases were found to enhance the likelihood of choosing DRT over public transport or biking. Furthermore, substitution pattern analysis highlights a stronger sensitivity to travel time changes than to travel cost variations in first/last-mile travel contexts. These insights provide valuable guidance for the effective integration of DRT into urban mobility systems.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".