Adaptive Traffic Signal Control With Deep Reinforcement Learning and High Dimensional Sensory Inputs: Case Study and Comprehensive Sensitivity Analyses
Bibliographic record
Abstract
Despite the constant rise in global urban populations and subsequent rise in transportation demand, significant expansion of infrastructure has been hampered by the constraints of space, cost, and environmental concerns. Therefore, optimizing the efficiency of existing infrastructure is becoming increasingly important. Adaptive traffic signal controllers aim to provide demand-responsive strategies to minimize motorists’ delay and achieve higher throughput at signalized intersections. With the advent of new sensory technologies and more intelligent control methods, the contribution of this paper is an adaptive traffic signal controller able to receive un-preprocessed high-dimensional sensory information such as GPS traces from connected vehicles and self-learn to minimize intersection delays. We use deep neural networks to operate directly on detailed sensory inputs and feed them into a reinforcement learning-based optimal control agent. The integration of these two components is known as deep learning. Using deep learning, we achieve two goals: (1) We eliminate the need for handcrafting a feature extraction process such as determining queue lengths, which is challenging and location-specific, and (2) we achieve better performance and faster training times compared to conventional tabular reinforcement learning approaches. We test our proposed controller against a tabular reinforcement learning agent, a reinforcement learning agent with a fully-connected Neural Network as a function approximator, and a state-of-practice, actuated traffic signal controller.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".