Modeling and analysis of freight mode choice behavior integrating grouped and repeated observations
Bibliographic record
Abstract
Modeling and understanding freight mode choice behavior is vital for policy makers and operators to design environment-friendly freight systems and improve service quality. This paper studies the shippers’ choice between truck and road-rail intermodal. Considering that a single shipper provides multiple groups of stated preference data under different transport contexts, a model integrating grouped and repeated observations (IGRO) is constructed based on a standard mixed logit (ML) model to capture the heterogeneity of intra-individual. A standard multinomial logit (MNL) model, a cross-sectional ML model and a standard panel ML model are also tested in this study. Statistical indicators show that the IGRO model performs the best, which indicates that the intra-individual heterogeneity across different transport contexts is stronger than the correlation. The potential factors contributing to the heterogeneity of intra-individual freight value of time (FVOT) are different origin-destinations (ODs) and cargo categories. In addition, a two-stage weighted calibration scheme is proposed. Subsequently, based on the calibrated model, some suggestions for increasing the intermodal market share are obtained through elasticity calculation and scenario analysis. Moreover, this paper measures carbon emission reduction under different modal shift scenarios and its monetary values.
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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 | 0.001 | 0.002 |
| 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".