СУЧАСНІ ТЕНДЕНЦІЇ АВТОМОБІЛІЗАЦІЇ ТА ЇХНІЙ ВПЛИВ НА РОЗВИТОК ТРАНСПОРТНОЇ ІНФРАСТРУКТУРИ
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the 21st century, global transport systems are undergoing a profound transformation driven by environmental challenges and technological advancements. Sustainable transport has emerged as a key development vector, involving not only the gradual elimination of internal combustion engines but also a redefinition of mobility principles.A central trend is the electrification of transport. Major automakers have announced plans to phase out petrol-powered vehicles, while charging infrastructure is rapidly expanding across Europe, North America, and China, enhancing the everyday practicality of electric vehicles.Digitalization is another crucial direction. Cities are adopting intelligent traffic management systems – adaptive traffic signals, real-time congestion monitoring, and smart parking solutions. Urban mobility control centers in cities such as Singapore, Barcelona, and Tokyo use AI and machine learning to streamline traffic flows, reduce emissions, and improve travel efficiency.The «Mobility as a Service» model is also gaining momentum. It offers users access to various transport modes – public transit, bike-sharing, car-sharing, and taxis – via a single platform or app. This system, already operational in Berlin, Helsinki, Stockholm, and Paris, promotes multimodal transport and reduces reliance on private vehicles.Equally important is the transformation of urban space. The «15-minute city» concept ensures essential services are reachable within 15 minutes on foot or by bike. Cities like Paris, Copenhagen, and Vancouver are redesigning public spaces to prioritize walkability, micromobility, and reduced car access in central zones.This article presents an analysis of key trends, current challenges, and development prospects of transport infrastructure in the context of growing motorization. Particular attention is given to identifying strategic pathways for transitioning toward sustainable and innovative mobility. It is emphasized that the decisions made today will have a direct impact on the quality, safety, and comfort of life in the near future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.011 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.072 | 0.018 |
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 it