Modelling Freight Transportation Impacts of the Gulf Cooperation Council Region using GIS
Bibliographic record
Abstract
Abstract Freight transportation is a key element in economic growth and the development of countries. Linking freight lines between Gulf Cooperation Council (GCC) countries (specifically Kuwait, Saudi Arabia, Bahrain, United Arab Emirates and Oman) will increase revenues, improve infrastructure asset efficiency and generate employment. Shipping and transport industries endure continuous changes and transformations in response to technological developments and changes in world trade. Significant amounts of documentation, research papers as well as technical and economic reports are available in literature that details ways in which these industries respond to and meet the needs of shippers and consumers. Information and data are valuable assets in providing a basic understanding and base knowledge of the different types and methods of shipping options, as well as the impacts of different transportation modes. In this paper, possible environmental impacts will be discussed, including air quality, noise levels and vibration. In addition, safety factors such as fatalities and accidents will be incorporated into discussions on mode selection. A GIS model was implemented and a case study was created to assess these impacts, estimate damage costs as well as discuss concerns with logistics and mode selection. Finally, possible mitigation measures to manage environmental and safety concerns will be proposed. Based on the analysis and results, more work on environmentally sustainable models and designs need to be implemented to mitigate impacts and to ensure that the environment and quality of life can be preserved. Incentivizing shifts from truck to rail or to intermodal systems is key for shippers’ participation in reducing environmental and societal impacts. Encouraging shippers to change their mode selection can relieve congestion and lower accident rates.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".