A Reinforcement Learning Based Traffic Control Strategy in a Macroscopic Fundamental Diagram Region
Bibliographic record
Abstract
Urban traffic control systems (UTCSs) are deployed to a great number of urban cities despite lacking feedback when adjusting the traffic signals. The development of reinforcement learning (RL) makes it possible to apply feedback to UTCS, and great efforts have been made on RL-based traffic control strategies. However, those studies are regardless of the traffic flow theory of the network and the road users’ perspectives on the performance of traffic. This study proposes a multiagent reinforcement learning (MARL) based traffic control strategy, in which each intersection in a macroscopic fundamental diagram (MFD) region was controlled by one agent using the level of services (LOS) and MFD-based parameters as rewards. The proposed MARL strategy was evaluated by simulation in a 3×3 grid network compared with pretimed, actuated, and MFD-based traffic control strategies. The evaluation results showed that, at different demand levels, the proposed MARL strategy outperforms the other three traffic control strategies in terms of average intersection queue length and average intersection waiting time to a different extent. Results also showed that the proposed MARL dissipated the congestion faster than the other three control strategies. Results of the Friedman test indicated that the differences in performances between the proposed MARL and other strategies were statistically significant regardless of the demand level. The MFD in the testbed network controlled by the proposed MARL was different from that controlled by the pretimed strategy, especially the MFD scatter plot. It provides insights on considering the traffic flow theory of the network when applying MARL to traffic control strategies.
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 | 0.000 | 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".