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

arXiv: https://arxiv.org/abs/2607.15862 · HTML: https://mlsanity.com/paper/2607.15862/

## 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.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 7 of 7)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey](https://mlsanity.com/paper/2005.00935/) (2020)
- [Position weighted backpressure intersection control for urban networks](https://mlsanity.com/paper/1810.11406/) (2018)
- [Eco-Driving at Signalized Intersections: A Multiple Signal Optimization Approach](https://mlsanity.com/paper/2001.01117/) (2020)
- [A Novel Max Pressure Algorithm Based on Traffic Delay](https://mlsanity.com/paper/2202.03290/) (2022)
- [CV-MP: Max-Pressure Control in Heterogeneously Distributed and Partially Connected Vehicle Environments](https://mlsanity.com/paper/2505.05258/) (2025)
- [Transit-MP: Transit-Prioritized Max-Pressure Control in Sparse Connected Vehicle Environments](https://mlsanity.com/paper/2511.00309/) (2025)

## 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.