Minimization of empty container truck trips: insights into truck-sharing constraints
Bibliographic record
Abstract
Purpose The issue of empty truck trips is largely ignored in the current literature. In order to cover this important research gap, the purpose of this paper is to explore, describe, categorize and rank the potential truck-sharing constraints for container trucks traveling empty around the port gates. Design/methodology/approach In order to contribute empirically to the current body of knowledge and understandings of truck-sharing constraints, this paper adopts a multi-method empirical approach involving both qualitative interviews and quantitative questionnaire surveys. Findings Among many key constraints that influence the future of truck-sharing opportunities, the authors determine, for example, that a carrier’s ability to earn the trust of its competitors is one of the top most important factors of success for a fruitful truck-sharing event. The problem is, perhaps, further complicated because of the increasing competitive environment in the container transport industry, as well as the lack of effective coordination between the key parties involved. Research limitations/implications None of the earlier studies has provided a broad understanding and ranking of the truck-sharing constraints that should be considered in truck-sharing events, although the empty trips issue has been limitedly mentioned in the recent academic literature. Practical implications Empty truck trips are wasted miles. Wasted empty miles decrease transport capacity in the container distribution chain along with causing an increase in carbon emission, traffic congestion, fuel consumption and environmental pollution. The research results can be used by policy makers to underpin effective measures to prevent the low utilization of trucks. Originality/value This study addresses an important gap. To the authors’ knowledge, this is the first study in the area that ranks truck-sharing constraints to reduce empty trucks trips.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".