Ambient Intelligence on Personal Mobility Assistants for Sustainable Travel Choices
Bibliographic record
Abstract
An increasing amount of attention is being paid by local public administrations, national and federal governments as well as by international institutions, such as the European Commission, to improve personal mobility within urban environments through the use of public transports.Improving mobility through increased use of public transportation is strategic to reduce energy consumption, to lower emissions and pollution levels, to improve public safety and to dramatically reduce congestions and road traffic.Reducing private transportation clearly brings significant benefits not only to citizens' quality of life and public health but it also results in a more efficient urban system as a whole, with consequent substantial economic benefits at the wider societal level.At the same time, it is difficult to change human habits and people using public transports should have an efficient and user friendly way to access the best travel options suitable for their needs.Based on this assumption, this paper presents a prototype for an ambient intelligent urban personal mobility assistant, i.e. a software for smartphones and tablets which promotes use of public transport by helping user to identify the best travel option across a multi-modal transport network, through a user-friendly interface that intelligently adjusts to user preferences, and behavior.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".