Traffic flow model of network simulators for estimation of CO<sub>2</sub> emissions
Bibliographic record
Abstract
SUMMARY This paper describes a new approach to developing a traffic flow model of traffic network simulators for the estimation of CO 2 emissions by considering various measures such as the use of vehicle control systems and transportation demand management (TDM) strategies. Thus far, we have developed the traffic network simulator NETSTREAM (NETwork Simulator for TRaffic Efficiency And Mobility), which has been able to reproduce traffic congestion in large‐scale networks because of its unique traffic flow model. This paper proposes an enhancement of this model: acceleration is also considered because it has a significant effect on the calculation of CO 2 emissions. The proposed model is then verified and validated. The results indicate that it can reproduce acceleration in a way that closely agrees with the actual data, while maintaining the Q–K and K–V relationships. Finally, this paper presents an example evaluation of an acceleration control system. We confirm that the proposed traffic flow model can evaluate environmental improvements in large‐scale networks. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".