Impact of Level 4 Automated Vehicles on Mode Choice Using a Needs‐Based Approach
Bibliographic record
Abstract
With the emergence of Level 4 automated vehicles, it is necessary to investigate the impact of these vehicles on mode choice. Previous studies have looked at the potential benefits and drawbacks of automated vehicles, but there has been little research done on how these vehicles will impact individuals’ travel behaviors. This paper proposes a needs‐based approach to study the impact of Level 4 automated vehicles on mode choice. The approach takes into consideration the travel needs of different individuals and their willingness to adopt new technologies. Through a stated preference survey in China, the data on travel preferences and the perceived safety levels of automated vehicles can be collected. Then, a model is built to simulate the adoption of Level 4 automated vehicles and estimate the mode split for different scenarios. The results indicated that private AV modes are preferred, and business and nonwork trips may be the targeted market for all AV modes. Overall, value of automation increases with income for private modes, with large variance. Furthermore, Pro‐AV attitude has a positive effect on value of automation, especially for self‐owned AV and AV subscription. Accordingly, the needs‐based approach demonstrates a promising method to study the impact of new technologies on travel behaviors and provides insights for policy makers to promote more sustainable transportation 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".