Traveler Information Provision for Incident Management: Implications for Vehicle Emissions
Bibliographic record
Abstract
The potential of intelligent transportation systems (ITS) in alleviating nonrecurring traffic congestion was assessed, and then the resulting implications for vehicle-induced emissions in a congested city in a developing country were estimated. This work provides a blueprint for future studies on both the evaluation of ITS deployment through dynamic traffic modeling and the assessment of resulting changes in travel times and emissions. The Greater Beirut, Lebanon, area road network was used as the test bed for evaluating strategies for incident management, which was the selected ITS application for this study. A series of simulation scenarios was conducted with dynamic traffic-simulation-assignment methodology, and resulting emissions were estimated with an emission-factor model. These scenarios were used to evaluate the effect of different ITS deployment parameters—such as type of information provision (pretrip and in-vehicle) and driver compliance—on network performance and resulting emissions. Network performance measures such as travel and stop times were developed, and corresponding vehicle emissions were estimated with carbon monoxide, nitrogen oxides, and total organic carbon as indicators for each scenario.
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".