Surrogate‐based traffic signal optimization (TSO) is a computationally efficient alternative to simulation‐based TSO. By replacing the simulation‐based objective function, a surrogate model can quickly identify solutions by searching for extreme points on its response surface. As a popular surrogate model, the ensemble of multiple diverse deep learning models can approximate complicated systems with a strong generalizability. However, existing ensemble methods barely focus on strengthening the prediction of extreme points, which we found can be realized by further diversifying base learners in the ensemble. The study proposes an adversarial diverse ensemble (ADE) method for online TSO with limited computational resources, comprising two stages: In the offline stage, base extractors are diversified with unlabeled data by a designed adversarial diversity training algorithm; in the online stage, base predictors are trained in parallel with limited labeled data, and the ensemble then serves as the surrogate model to search for solutions iteratively for TSO. First, it is demonstrated that the prediction accuracy on extreme points, and associated solution quality, can be constantly improved with base learners’ diversity enhanced by ADE. Case studies of TSO conducted on a four‐intersection arterial further demonstrate the superior solution quality and computational efficiency of the ADE surrogate model in a wide range of traffic scenarios. Moreover, a large‐scale online TSO experiment under dynamic traffic demand proves ADE's effectiveness in practical applications.

中文翻译：

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一种用于基于代理的交通信号优化的对抗性多样化深度集成方法


基于代理的交通信号优化 （TSO） 是基于仿真的 TSO 的一种计算高效的替代方案。通过替换基于仿真的目标函数，代理模型可以通过在其响应面上搜索极值点来快速识别解。作为一种流行的代理模型，多个不同深度学习模型的集合可以近似复杂系统，具有很强的泛化性。然而，现有的集成方法几乎不专注于加强对极值点的预测，我们发现这可以通过进一步使集成中的基本学习者多样化来实现。该研究提出了一种计算资源有限的在线 TSO 的对抗性多样化集成 （ADE） 方法，包括两个阶段：在离线阶段，通过设计的对抗性多样性训练算法，用未标记的数据进行碱基提取器的多样化;在在线阶段，碱基预测器与有限的标记数据并行训练，然后集成作为代理模型，迭代搜索 TSO 的解。首先，它证明了极值点的预测准确性和相关解决方案质量可以随着 ADE 增强的基本学习器的多样性而不断提高。在四交叉干道上进行的 TSO 案例研究进一步证明了 ADE 代理模型在各种交通场景中的卓越求解质量和计算效率。此外，动态流量需求下的大规模在线 TSO 实验证明了 ADE 在实际应用中的有效性。