More space and improved living conditions in cities with autonomous vehicles
Bibliographic record
Abstract
Many people live in cities today.Many more will do so in future.This increases the demand for space and (space for) transport.Space to expand roads is usually scarce.Building tunnels or elevated bridges is very expensive.Solving one bottleneck creates another bottleneck downstream.More road infrastructure leads to more cars and more cars to more congestion and externalities.Megacities invest in large-scale (preferably underground) public transport while smaller cities lack the required number of travellers and the money to warrant the high investment costs.In a sustainable city, the supply of goods, services, water, energy and transportation should differ from the current practice.New technologies in car manufacture are interesting, in particular, the nearly roadworthy self-driving (autonomous) cars.It is still a demanding challenge to let these share roads with conventional cars, cyclists and pedestrians.Currently, cities lack the space for a separate network for these cars.A socially challenging alternative would be to replace all private cars by shared electric self-driving cars and small shuttle buses and integrate these with mass transport, cycling and walking.Passenger transport would need much less space for driving and parking.Congestion will vanish.Local air pollution, noise and use of resources to produce cars and road materials will be reduced.Reclaimed space can be used to create a more sustainable and social environment and to optimize city logistics.The building blocks of such a (public-private) system exist already or will become available in the 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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".