Framework of a comparative life cycle analysis for rail and road freight transport
Bibliographic record
Abstract
Transport plays a central role in the development of economies and people around the world, by adding value to goods and developing industries, among other economic benefits.Selection of transportation modes are of significant interest to shippers, government actors and the general public due to both costs and impacts.The main objectives of this thesis are two-fold.First, is to develop a framework of economic life-cycle analysis focusing on rail and trucking systems to identify the most viable and reliable land transportation mode for moving goods based on hauling distances and freight loads.The second objective is to build a comprehensive model to estimate the shipping life cycle cost for design strategies using statistical procedures and GIS software applications.Contributions of this thesis include: (1) Development of a Life Cycle Cost Analysis (LCCA) model that includes all of the direct costs, indirect costs and pollution produced by using rail and trucking freight transportation systems; (2) Determination of effect of issue of border delays, which is one of the main constraints when border inspection stations are factored into shipment processes; (3) Planning a future rail network that passes through different countries using ArcGIS; (4) Application of the reliability and sensitivity analysis using the implemented model; (5) Development of logistics applications using different scenarios that help to evaluate intermodal yard locations, alternative route selection, risk and warehouse facility locations; and (6) Establishment of guidelines for environmentally sustainable and reliable freight transportation systems to aid transportation engineers and policy makers.Data from different countries, municipalities and companies within the Gulf Cooperation Council region were used to build the model and conduct the analysis.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 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".