Reducing congestion during road works through travel demand management
Bibliographic record
Abstract
Travel demand management research indicates that a quarter of drivers in London are willing and are able to change their behaviour when provided with persuasive travel advice. Enhanced information and travel advice about road works at a site in London in 2015 were communicated to the public, to mitigate congestion and to reduce customer impact. Data from automatic number plate recognition cameras from a neutral period were compared with data from the first week of road works to determine the rate of change in drivers’ behaviour. The same comparison was carried out for a control site without enhanced communications, and the difference between the rates of change was calculated to determine the effect of better communications. The data indicated that around 14% of frequent drivers were seen to have changed their time of travel or route through the road works, as a result of better communications. The monetised social benefit of that behaviour change set against the cost of enhanced communications generated a benefit:cost ratio of more than 4:1. Customer surveys indicated that the reputation of the highway authority was also upheld through the work periods.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".