The Effect of Driver Behavior on Energy Consumption Using a Microscopic Traffic Model
Bibliographic record
Abstract
Driver behavior significantly affects vehicle energy consumption and emissions which are critical global concerns. This paper proposes a microscopic traffic model to characterize energy consumption considering the physiological and psychological behavior of a driver. Physiological behavior reflects driver response to traffic conditions while psychological behavior includes driver sensitivity and awareness. Thus, the proposed model encompasses driver response, sensitivity, and awareness. Data was obtained using a sensor node installed at the roadside. Traffic was monitored for six days and the data was modeled using regression analysis. The models and vehicle fuel consumption rate are integrated into the proposed model. The Intelligent Driver (ID) model is based on a constant exponent so driver behavior does not vary with traffic conditions. Thus, fuel consumption is ignored. It is shown that the proposed model results in stable traffic, i.e. traffic returns to a steady state following a disturbance, whereas the ID model produces unstable traffic behavior. Further, it results in smaller fluctuations in speed, flow, and density. Thus, the proposed model can be used to lower fuel consumption and emissions, thereby reducing air pollution and contributing to climate change mitigation.
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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".