Use of social media for assessing sustainable urban mobility indicators
Bibliographic record
Abstract
Achieving sustainable urban mobility is a complex and multivariate issue that requires constant monitoring and evaluation of the existing situation and possible reconsideration and adjustment of objectives and strategy.The use of indicators is perhaps the most common methodological assessment tool for the sustainable urban mobility level achieved.Key performance indicators can provide in a simple way useful information for complex phenomena in an urban area (i.e.identification of the specific problems and their development over time).Thus, they contribute at a great degree to the decisions made concerning the prioritization of measures and policies toward achieving a goal.However, the use of indicators often constitutes a highly time consuming and costly process due to the large volumes of raw data required for their calculation.In recent years, a solution toward this problem is attempted to be given through the adoption of new technologies and approaches, such as the collection and export of 'big data' from social networks such as Facebook, Twitter, etc. Social networks provide to their users a continuous and enhanced ability for communication, interface and interaction.Such networks are therefore an important potential tool for the promotion of research in the transport sector, as the amount of data generated in their context gives the possibility to analyse and investigate with greater precision critical issues (e.g.trips characteristics) of urban mobility.The present study is an attempt to link the indicators related to sustainable mobility with social networks.The main advantage resulting from the above link, beyond the possibility of a more precise evaluation of the indicators, is to highlight the society's position toward the prioritization of the various transport-related aspects and measures.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".