mlsanityData to 14 Oct 2026

PriEco-DRL: Joint Optimization of Electric-Bus Eco-Driving and Transit-Priority Adaptive Signals via Deep Reinforcement Learning

arXiv 2607.15862 ↗First posted 17 Jul 2026math.OCcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

Urban transit electrification requires balancing energy efficiency, schedule reliability, and ride comfort for electric buses (EBs), particularly when interacting with transit-priority adaptive signals in congested networks. This paper proposes PriEco-DRL, a joint optimization framework that integrates EB eco-driving with transit-priority adaptive signal control using deep reinforcement learning (DRL). The signal layer employs a priority-weighted max-pressure (Priority-MP) controller to allocate green time based on occupancy-aware pressures, while the vehicle layer adapts longitudinal control based on uncertain and dynamically evolving local signal cues. A structured reward combines guidance and event-based reinforcement to align EB arrivals with green opportunities while considering energy, time, comfort, and safety. The framework uses centralized training and decentralized execution (CTDE) with parameter sharing, allowing a single DRL agent to learn from multiple buses and routes using local observations. Experiments on a real-world corridor show that PriEco-DRL reduces EB energy consumption while maintaining network efficiency and transit priority compared with fixed-time, actuated, and rule-based signal-vehicle coordination baselines. Energy- and trajectory-based analyses reveal that the improvements stem from fewer unscheduled stop-start events and smoother speed regulation under adaptive signals. The results highlight a tunable energy-time trade-off, allowing flexible operational choices through reward weighting.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 7 of its 55 references that are in the map. First posted on arXiv 17 Jul 2026.

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7 of 55 references.

References · 7

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  2. 2020Deep Reinforcement Learning for Intelligent Transportation Systems: A Surveycited by 98
  3. 2018Position weighted backpressure intersection control for urban networkscited by 5
  4. 2020Eco-Driving at Signalized Intersections: A Multiple Signal Optimization Approachcited by 4
  5. 2022A Novel Max Pressure Algorithm Based on Traffic Delaycited by 3
  6. 2025CV-MP: Max-Pressure Control in Heterogeneously Distributed and Partially Connected Vehicle Environmentscited by 2
  7. 2025Transit-MP: Transit-Prioritized Max-Pressure Control in Sparse Connected Vehicle Environmentscited by 1

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  1. 2021A Deep Reinforcement Learning Framework for Eco-driving in Connected and Automated Hybrid Electric Vehicles
  2. 2025Safe and Sustainable Electric Bus Charging Scheduling with Constrained Hierarchical DRL
  3. 2025Optimizing Electric Bus Charging Scheduling with Uncertainties Using Hierarchical Deep Reinforcement Learning
  4. 2024Adaptive Transit Signal Priority based on Deep Reinforcement Learning and Connected Vehicles in a Traffic Microsimulation Environment
  5. 2021Safe Model-based Off-policy Reinforcement Learning for Eco-Driving in Connected and Automated Hybrid Electric Vehicles
  6. 2022Driver Assistance Eco-driving and Transmission Control with Deep Reinforcement Learning
  7. 2025Electric Bus Charging Schedules Relying on Real Data-Driven Targets Based on Hierarchical Deep Reinforcement Learning
  8. 2022Eco-driving for Electric Connected Vehicles at Signalized Intersections: A Parameterized Reinforcement Learning approach