Estimation of logit models for different types of freight in urban freight transport: a new discrete choice model group for Istanbul
Bibliographic record
Abstract
Freight transportation plays a significant role in urban transport and contributes substantially to environmental issues due to the use of heavy vehicles. However, compared to passenger transport, decision-makers often overlook the impact of freight transport despite its contribution to congestion. The leading factors influencing the selection of freight vehicles include cost, freight size, and packaging characteristics. However, existing transportation studies mainly examine freight vehicle preferences at a general level, needing a comprehensive understanding of freight transportation. To address this gap, through the framework we propose in this study, we present a discrete modeling-based methodology to identify the factors that determine freight vehicle preferences for shipping and sender firms with the ultimate motivation of exploring the significant factors to ensure more efficient freight transportation. The presented framework is tested through a real-world case study conducted in Istanbul, Turkey, utilizing field surveys conducted by the Municipality within the context of a newly developed Logistics Master Plan for the city. Two of the four freight model structures created gave outputs highly accepted in the literature. Interestingly, the findings deviated from the existing literature, revealing that the choice of freight vehicles is influenced by different packaging variables depending on the freight type being transported.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".