Neighborhood-Coordinated Multiagent Reinforcement Learning for Networked Adaptive Traffic Signal Control
Bibliographic record
Abstract
Traffic congestion in Greater Toronto Area costs Canada $ 6 billion /year and is expected to grow up to $ 15 billion /year in the next few decades. Adaptive traffic signal control is a promising technique to alleviate traffic congestion. For medium-large transportation networks, coordinated adaptive traffic signal control is becoming a challenging problem because number of system states and actions all grow exponentially as the number of networked intersections grows. Efficient and robust controllers can be designed using a multi-agent reinforcement learning (MARL) approach in which each controller (agent) is responsible for the control of traffic lights around a single traffic junction. Although similar approaches are investigated in the literature, agents select locally optimal decisions (actions) without coordination with their neighbors. This paper presents a novel, decentralized and coordinated adaptive real-time traffic signal control system using Neighborhood-Coordinated MARL (NC-MARL) that aims to minimize the total vehicle delay in the network. The system is tested using microscopic traffic simulation software (PARAMICS) on a network of 5 signalized intersections in Downtown Toronto. The performance of NC-MARL is compared against two approaches: the conventional pretimed signal control (B1) and independent RL-based control agents (B2), i.e. with no coordination. The results show that network-wide average delay savings range from 32% to 63% relative to B1 and from 7% to 12% relative to B2 under different demand levels and arrival profiles.
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".