Large scale flow modelling and control: a macroscopic fundamental diagram approach
Bibliographic record
Abstract
In recent decades, traffic congestion has become a major issue in traffic networks, especially in urban networks comprised of a set of short links and signalized intersections. To circumvent the issue, various traffic control and management strategies have been devised; however, the proposed strategies are rarely developed at network-wide level. Further, the modelling approach is based on microscopic models that cannot be adopted for centralized control or macroscopic models that have limited capacity to properly describe important phenomenon of traffic networks. This thesis aims at modelling and control of a large-scale urban network comprised of multiple pockets of congestion. The modelling approach is based on macroscopic fundamental diagram (MFD), which assumes a well-defined relationship between average flow and average density for any traffic network with spatially homogeneous distribution of vehicles. This simpler representation of large-scale traffic networks using aggregated traffic variables facilitates a centralized and real-time control of urban networks. To use the system-wide benefits of MFD models, firstly, an anticipatory control scheme, integrating road users routing responses to the control model is advanced. The proposed anticipatory control approach is found to produce globally optimal solutions and move the network towards system optimum traffic condition. Thereafter, a proportionally fair control scheme that simultaneously enhances efficiency and fairness among road users is developed. The unique feature of the developed perimeter controller is consideration of road users' trip utility in the control model without much sacrificing efficiency for fairness. Despite the computational advantages of aggregated MFD models, these models do not describe important phenomenon of traffic networks. In the final part of this thesis, the MFD dynamics is enhanced to capture multiple kinematic waves, congestion, and queueing with high precision and within reasonable computational effort. Further, an approach for incorporating connected/autonomous vehicles (CAV)s into MFD dynamics is introduced; the network-wide effect of CAVs on network's traffic state is then investigated.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".