Preference-Conditioned Multi-Objective Reinforcement Learning for Runtime-Tunable Transit Signal Priority
Introduces a preference-conditioned multi-objective RL controller for Transit Signal Priority (TSP) that enables runtime tuning of the trade-off between bus delay reduction and general traffic impact without retraining. Implements the solution within the IntersectionZoo framework using a constrained signal-control wrapper and enhances training data via bus-prevalence augmentation and timetable-based bus insertion to handle sparse transit events. Demonstrates superior performance over fixed-time
Analysis
TL;DR
- Introduces a preference-conditioned multi-objective RL controller for Transit Signal Priority (TSP) that enables runtime tuning of the trade-off between bus delay reduction and general traffic impact without retraining.
- Implements the solution within the IntersectionZoo framework using a constrained signal-control wrapper and enhances training data via bus-prevalence augmentation and timetable-based bus insertion to handle sparse transit events.
- Demonstrates superior performance over fixed-time controls, rule-based overlays, and fixed-weight PPO specialists by maintaining a smooth empirical trade-off frontier while strictly adhering to signal phase constraints.
- Reveals through tail-delay diagnostics that while moderate preference settings limit negative externalities on non-bus traffic, high bus-priority weights can significantly increase delays for other road users.
Why It Matters
This research addresses a critical operational limitation in current AI-driven traffic management systems: the inability to adapt quickly to changing agency priorities or real-time disruption conditions without costly retraining. By decoupling policy learning from specific objective weighting, it offers a flexible, deployable solution for smart city infrastructure where traffic patterns and policy goals fluctuate dynamically.
Technical Details
- Model Architecture: A preference-conditioned policy $\pi(a \mid s,w)$ is trained to select signal phases based on state $s$ and a continuous preference parameter $w$, allowing for continuous adjustment of the reward scalarization at inference time.
- Constraint Handling: The approach integrates a constrained signal-control/TSP wrapper within the IntersectionZoo environment, ensuring that all actions respect minimum/maximum green times and transition feasibility constraints.
- Data Augmentation: To mitigate the sparsity of transit-priority events during training, the authors employ bus-prevalence augmentation and timetable-based bus insertion techniques to create more representative and frequent training scenarios.
- Benchmarking: The method is evaluated against three baselines: fixed-time control, a rule-based TSP overlay, and fixed-weight Proximal Policy Optimization (PPO) specialists, showing consistent outperformance in terms of constraint satisfaction and adaptive efficiency.
Industry Insight
- Operational Agility: Traffic management agencies can adopt a single trained model to serve diverse operational needs (e.g., rush hour vs. off-peak) by simply adjusting input parameters, reducing the need for maintaining multiple specialized models.
- Risk Management: Practitioners should monitor tail-delay metrics closely when setting high bus-priority weights, as aggressive optimization for transit can lead to disproportionate negative impacts on private vehicle throughput, potentially affecting public acceptance.
- Implementation Strategy: Leveraging existing frameworks like IntersectionZoo lowers the barrier to entry for deploying complex RL controllers, suggesting that modular, constraint-aware wrappers are a viable path for integrating AI into legacy traffic signal systems.
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