Stimulus Perception in Long-Distance Railway Mode Choice
Bibliographic record
Abstract
In the context of countries in the so-called Global South, where passenger railway services are either nonexistent or poorly performed, discrete choice models are useful to identify the attributes that affect users’ choices and provide insights on their behaviour in regional long-distance trips. Several theories and models have been proposed to understand travel behaviour for effective strategical decision in the transport field. The well-knownRandom Utility Maximization (RUM) approach has been widely used for such purposes, while the Random Regret Minimization (RRM) approach has been recently explored in the literature. However, the magnitude in the difference of levels of the attributes, or the stimulus perception, may affect the results of such models and biases the estimations. Therefore, this paper aims to assess the stimulus perception in mode choice to compare conventional rail (CR) and high-speed rail (HSR) services for passenger transport in intercity trips in Brazil. Estimations of RUM and RRM models were performed with a dataset from a stated preference survey comparing two railway technologies (CR and HSR) with other modes of transport (car, bus, and airplanes) for long-distance trips in the Southeast region of Brazil. Findings provide useful insights about the impacts of travel costs, travel times, and frequency of services, as well as sociodemographic characteristics of users. From the modelling outputs, it was found that users are affected by the magnitude of travel costs, time, and frequency only in business trips by HSR in the Brazilian context.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".