Digitization in the Transport Sector: Development Trends and Indicators. Part 1
Bibliographic record
Abstract
The article outlines the current global trends in digitalization, which include the use of big data and cloud technologies, the spread of Internet of Things (IoT), the development of robotics, the spread of 3D printing technology, blockchain processes and crowdsourcing. The main problem of the article is to study the process of digitalisation in general and in the transport sector in the context of trends and development indicators, and to make recommendations for further improvement of the national statistical database by including indicators on the development of information and communication technologies in the transport sector based on international databases. The particular attention is paid to the consideration of the institutional basis of digitalisation worldwide, with focus on the practices of the EU, Germany, Canada, the USA and Kazakhstan. The experience of Ukraine in digitization of the economy and the transport sector in particular is carefully studied. Consideration is given to the database indicators measuring digitalization trends, with selecting the indicators reflecting these processes in individual economies and related to digital transformations in the transport. The particular attention is paid to the Ukraine’s position in these international databases and the completeness of information on relevant indicators in Ukraine. A comparison of the selected indicators with the indicators of digitalisation in the transport sector in the official statistical database was carried out, and the systematization of these indicators was made in order to further improve the official statistical database by including in it the indicators on the development of information and communication technologies in the transport sector. The careful study and analysis of international and national statistical databases allowed for creating a set of indicators on digitalization in the transport sector, with including the indicators in it reflecting the dissemination of information and communication technologies in the transport sector and characterizing digital transformations in the transport. The proposed set of indicators is dynamic and can be complemented by other indicators in the process of digital transformations in the transport sector. Given the current global trends of the growing penetration of digital technology in all the spheres of human activity, this set of indicators can be used not only to monitor these processes in the transport sector, but also in the management practices.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.017 | 0.052 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".