A macroscopic dynamic network loading model using variational theory in a connected and autonomous vehicle environment
Why this work is in the frame
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Bibliographic record
Abstract
Recent studies on macroscopic fundamental diagram (MFD) have shifted towards enhancing MFD dynamics in terms of efficiency and realism. In this paper, a multi-reservoir dynamic network loading (MRDNL) model for a large-scale urban road network is developed. The proposed framework consists of a hyper-link and a hyper-node model. The hyper-link model is constructed using variational theory (VT) by introducing a set of constraints that construct an upper bound for upstream and downstream cumulative accumulations. Using VT, LWR is incorporated into MFD dynamics to enhance traffic propagation at reservoirs. The hyper-link model captures important traffic phenomena, such as kinematic waves, queueing, and congestion, while the hyper-node model functions as a medium for transferring flow to other parts of the multi-reservoir urban network. A numerical scheme is advanced for solving the proposed MRDNL model and its performance is evaluated using a hypothetical traffic network. Compared to previous MFD-based dynamic network loading models, our approach is more computationally efficient since it does not require discretization and offers more realistic solutions by considering multiple kinematic waves and including the dynamics of urban traffic signals. This study, further, incorporated connected and autonomous vehicles (CAVs) into MFD dynamics and evaluated the network-wide effect of introduction of CAVs in urban traffic networks. For a network with 100% CAV market penetration rate, an approximate enhancement of 30% in total network outflow was found.
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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 | 0.001 | 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 it