Modal Choice for the Driverless City: Scenario Simulation Based on a Stated Preference Survey
Bibliographic record
Abstract
The possible future introduction of Autonomous Vehicles (AVs) and Shared Autonomous Vehicles (SAVs) raises questions about how they might affect the demand for transport and especially modal choice. In this research, a stated preference (SP) survey and a modelling process using Mixed Logit are proposed to simulate the future market share of AVs/SAVs and how their introduction into the system could change the modal choice, especially in relation to active and public transport modes. An efficient SP survey design has been developed based on the state-of-the-art information and carried out in 2020 among citizens of two medium-sized Southern European cities within a car-intensive region. The design considered different trip purposes (compulsory, leisure), different trip distances, and attributes not taken into account before, such as comfort and the physical characteristics of the terrain for the active modes. The model results suggest that AVs and SAVs were the preferred transport modes for most respondents, accounting for more than 58% of the market share in the scenarios presented. Also, we detected some socioeconomic differences in the propensity to use this mode of transport showing that men living in high-income households and car users were more prone to use autonomous alternatives. The models allowed us to simulate different scenarios, such as experiencing higher costs for using the AV alternative. Policies imposing a higher cost for the AV alternative but lower costs and waiting times for the SAV and public transport alternatives could decrease the AV’s market share favouring more sustainable modes. The above scenario showed that achieving a more sustainable future mobility system considering AVs requires an in-depth transport demand knowledge and adequate transport policies.
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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 | 0.000 |
| 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".