用可变路网划分与多通道状态表示，提升交通信号自适应控制效果。
Adaptive traffic signal control optimization using a novel road partition and multi-channel state representation method
- 提出对数加线性组合的动态路网划分法，适配不同交通密度。
- 多通道状态输入包含车流数、平均速度和空间占有率，优化信号配时。
- 在SUMO仿真中验证跨区域迁移能力，性能优于固定路段划分方法。
本文提出一种基于深度强化学习的自适应交通信号控制方法，结合深度Q网络（DQN）与近端策略优化（PPO），通过引入可变单元长度的路网划分和多通道状态表征来优化信号配时。提出由对数函数与线性函数之和构成的路网划分公式，状态变量为车辆数、平均速度与空间占有率组成的三通道向量。动作空间由可用信号相位构成，选定相位以固定绿灯时间执行。奖励函数采用关键交通指标的绝对值进行加权：等待时间、速度与燃油消耗，各指标经典型最大值归一化后赋予优先级权重。基于Sumo-TensorFlow-Python的仿真结果表明，所提方法具备跨区域迁移能力，其优化性能显著优于固定单元长度方案。
原文摘要 · Abstract (English)
This study proposes a novel adaptive traffic signal control method leveraging a Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) to optimize signal timing by integrating variable cell length and multi-channel state representation. A road partition formula consisting of the sum of logarithmic and linear functions was proposed. The state variables are a vector composed of three channels: the number of vehicles, the average speed, and space occupancy. The set of available signal phases constitutes the action space, the selected phase is executed with a fixed green time. The reward function is formulated using the absolute values of key traffic state metrics - waiting time, speed, and fuel consumption. Each metric is normalized by a typical maximum value and assigned a weight that reflects its priority and optimization direction. The simulation results, using Sumo-TensorFlow-Python, demonstrate a cross-range transferability evaluation and show that the proposed variable cell length and multi-channel state representation method excels compared to fixed cell length in optimization performance.
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